Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Related tags

Deep Learning python jupyter bayes bayesian-statistics
Overview

Think Bayes 2

by Allen B. Downey

The HTML version of this book is here.

Think Bayes is an introduction to Bayesian statistics using computational methods.

Think Bayes is a Free Book. It is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), which means that you are free to copy and modify it, as long as you attribute the work and don’t use it for commercial purposes.

Other Free Books by Allen Downey are available from Green Tea Press.

Run the notebooks

Download the notebooks as a Zip file

Or use these links to run the notebooks on Colab:

Comments
  • Chapter 1 download data code isn't working

    Chapter 1 download data code isn't working

    I found that the original code seems to be outdated since it seems to use a different repository name (BiteSizeBayes) and it uses a csv file whereas the current data is in a gss_bayes folder which I don't know how to read/use (normally I use csv files). Can anyone give guidance on how to read in this data (e.g. docs) or a pointer on where to find the original GSS data? Thanks in advance.

    Here's the original code for context:

    `# Load the data file

    from os.path import basename, exists

    def download(url): filename = basename(url) if not exists(filename): from urllib.request import urlretrieve local, _ = urlretrieve(url, filename) print('Downloaded ' + local)

    download('https://github.com/AllenDowney/BiteSizeBayes/raw/master/gss_bayes.csv')`

    opened by HelloWorld183L 5
  • Chapter 20 Counting Cells Measurement Error Specification

    Chapter 20 Counting Cells Measurement Error Specification

    I love your work and own a hard copy of Think Bayes 2! I know you got the cell counting example from another fantastic content creator. I have been pondering inferred vs. known error in my models and I think that the pymc cell counting model can be improved for posterity. Mainly, I think that there are a bunch of parameters that are inferred in pymc3 that should be fixed. For example:

    with pm.Model() as model:
    yeast_conc = pm.Normal("yeast conc", 
                           mu=2 * billion, sd=0.4 * billion)

    shaker1_vol = pm.Normal("shaker1 vol", 
                               mu=9.0, sd=0.05)
    shaker2_vol = pm.Normal("shaker2 vol", 
                               mu=9.0, sd=0.05)
    shaker3_vol = pm.Normal("shaker3 vol", 
                               mu=9.0, sd=0.05)

    should be:

    import theano
shaker_vol_mu = theano.shared(9.0)
shaker_vol_sd = theano.shared(0.05)

with pm.Model() as model:
    yeast_conc = pm.Normal("yeast conc", 
                           mu=2 * billion, sd=0.4 * billion)

    shaker1_vol = pm.Normal("shaker1 vol", 
                               mu=shaker_vol_mu, sd=shaker_vol_sd)
    shaker2_vol = pm.Normal("shaker2 vol", 
                               mu=shaker_vol_mu, sd=shaker_vol_sd)
    shaker3_vol = pm.Normal("shaker3 vol", 
                               mu=shaker_vol_mu, sd=shaker_vol_sd)

    I'm interpreting the mean and sd of yeast conc as prior values to be inferred, and the mean and sd of shaker volumes as fixed values based on information from outside of the model inference (e.g., the values listed on the package of a pipette or something). I think this is also consistent with ABC portion of the analysis present just after. Other lines of code that would be added include the following:

    shaker_transfer_mu = theano.shared(1.0)
shaker_transfer_sd = theano.shared(0.01)

chamber_vol_mu = theano.shared(0.0001)
chamber_vol_sd = theano.shared(0.0001/20)

    associated pm.Normal() calls would be updated.

    My motivation for writing is the amount of time it took for me to figure out how and when to specify fixed vs. inferred parameter values in my models. Thanks!

    opened by gbrunkhorst 5
  • TypeError: Series.__init__() got an unexpected keyword argument 'normalize'

    TypeError: Series.__init__() got an unexpected keyword argument 'normalize'

    Hi, Professor. I am facing this problem when I am practicing probability mass function in gss.hdf5. I am following exactly what have you teach in the slides. May I know why is this happening? Thank you.

    import pandas as pd
import matplotlib.pyplot as plt
from empiricaldist import Pmf

    gss = pd.read_hdf('gss.hdf5','gss')
educ =gss['educ']
pmf_educ = Pmf(educ normalize=False)

    Traceback (most recent call last): File "c:\Users\User\Desktop\Study\Data_Camp\courses\Exploratory Data Analysis in Python\Probability_mass_functions.py", line 30, in pmf_educ = Pmf(educ, normalize=False) File "C:\Users\User\AppData\Local\Programs\Python\Python310\lib\site-packages\empiricaldist\empiricaldist.py", line 45, in init super().init(*args, **kwargs) TypeError: Series.init() got an unexpected keyword argument 'normalize'``

    opened by Strigfox85 2
  • Update conda env

    Update conda env

    I really enjoyed reading Think Bayes, and I saw your tweet about conda being slow, so I wanted to help out. Please let me know if you'd like me to make any changes, and feel free to close if it isn't helpful.

    Background

    The biggest change I made was to ignore the defaults channel with nodefaults. At best searching the defaults channel will waste time, and at worst it will pull in defaults packages that are incompatible with conda-forge packages (they are built with different compilers). The only reason to keep defaults in the list is occasionally some packages required for Windows compatibility are only available from defaults (but I confirmed that this wasn't the case).

    By listing conda-forge first, this channel is always searched first. The fastai channel is then consulted whenever a package can't be found in the conda-forge channel.

    Demonstration

    I purposely tested on Windows since this is likely a big motivator for using conda in the first place. mamba installed the environment in under 5 minutes. And this is worst case scenario since it had to index the channels and download all the packages (ie nothing was cached). And I agree with you that conda is unuseable in this situation. I let it spin for ~15 minutes before I just killed it.

    # Cleaning up from my previous experiments
conda env remove -n ThinkBayes2
conda clean -ya

mamba --version
## mamba 0.23.0
## conda 4.12.0

# Run from the Anaconda prompt
Time
## The current time is: 11:42:58.56
mamba env create --file environment.yml
Time
## The current time is: 11:47:21.52
    conda list -n ThinkBayes2 
    conda list -n ThinkBayes2
# Name                    Version                   Build  Channel
alabaster                 0.7.12                     py_0    conda-forge
anyio                     3.5.0           py310h5588dad_0    conda-forge
argon2-cffi               21.3.0             pyhd8ed1ab_0    conda-forge
argon2-cffi-bindings      21.2.0          py310he2412df_2    conda-forge
arviz                     0.11.1             pyhd8ed1ab_0    conda-forge
asttokens                 2.0.5              pyhd8ed1ab_0    conda-forge
atomicwrites              1.4.0              pyh9f0ad1d_0    conda-forge
attrs                     21.4.0             pyhd8ed1ab_0    conda-forge
babel                     2.9.1              pyh44b312d_0    conda-forge
backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
backports                 1.0                        py_2    conda-forge
backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
beautifulsoup4            4.11.1             pyha770c72_0    conda-forge
bleach                    5.0.0              pyhd8ed1ab_0    conda-forge
blosc                     1.21.1               hcbbf2c4_0    conda-forge
brotli                    1.0.9                h8ffe710_7    conda-forge
brotli-bin                1.0.9                h8ffe710_7    conda-forge
brotlipy                  0.7.0           py310he2412df_1004    conda-forge
bzip2                     1.0.8                h8ffe710_4    conda-forge
ca-certificates           2021.10.8            h5b45459_0    conda-forge
cached-property           1.5.2                hd8ed1ab_1    conda-forge
cached_property           1.5.2              pyha770c72_1    conda-forge
cairo                     1.16.0            h15b3021_1010    conda-forge
certifi                   2021.10.8       py310h5588dad_2    conda-forge
cffi                      1.15.0          py310hcbf9ad4_0    conda-forge
cftime                    1.6.0           py310h2873277_1    conda-forge
charset-normalizer        2.0.12             pyhd8ed1ab_0    conda-forge
click                     7.1.2              pyh9f0ad1d_0    conda-forge
click-completion          0.5.2           py310h5588dad_3    conda-forge
click-log                 0.3.2              pyh9f0ad1d_0    conda-forge
colorama                  0.4.4              pyh9f0ad1d_0    conda-forge
cryptography              36.0.2          py310ha857299_1    conda-forge
curl                      7.83.0               h789b8ee_0    conda-forge
cycler                    0.11.0             pyhd8ed1ab_0    conda-forge
dataclasses               0.8                pyhc8e2a94_3    conda-forge
debugpy                   1.6.0           py310h8a704f9_0    conda-forge
decorator                 5.1.1              pyhd8ed1ab_0    conda-forge
defusedxml                0.7.1              pyhd8ed1ab_0    conda-forge
docutils                  0.15.2          py310h5588dad_3    conda-forge
empiricaldist             0.6.3                    pypi_0    pypi
entrypoints               0.4                pyhd8ed1ab_0    conda-forge
executing                 0.8.3              pyhd8ed1ab_0    conda-forge
expat                     2.4.8                h39d44d4_0    conda-forge
fastcore                  1.4.2              pyhd8ed1ab_0    conda-forge
fastdoc                   0.0.2                      py_0    fastai
fastprogress              1.0.2              pyhd8ed1ab_0    conda-forge
fastrelease               0.1.16                     py_0    fastai
flit-core                 3.7.1              pyhd8ed1ab_0    conda-forge
font-ttf-dejavu-sans-mono 2.37                 hab24e00_0    conda-forge
font-ttf-inconsolata      3.000                h77eed37_0    conda-forge
font-ttf-source-code-pro  2.038                h77eed37_0    conda-forge
font-ttf-ubuntu           0.83                 hab24e00_0    conda-forge
fontconfig                2.14.0               hce3cb01_0    conda-forge
fonts-conda-ecosystem     1                             0    conda-forge
fonts-conda-forge         1                             0    conda-forge
fonttools                 4.33.3          py310he2412df_0    conda-forge
freetype                  2.10.4               h546665d_1    conda-forge
fribidi                   1.0.10               h8d14728_0    conda-forge
getopt-win32              0.1                  h8ffe710_0    conda-forge
gettext                   0.19.8.1          ha2e2712_1008    conda-forge
ghapi                     0.1.20             pyhd8ed1ab_0    conda-forge
ghp-import                2.1.0              pyhd8ed1ab_0    conda-forge
gitdb                     4.0.9              pyhd8ed1ab_0    conda-forge
gitpython                 3.1.27             pyhd8ed1ab_0    conda-forge
graphite2                 1.3.13                     1000    conda-forge
graphviz                  3.0.0                had6c3a3_1    conda-forge
greenlet                  1.1.2           py310h8a704f9_2    conda-forge
gts                       0.7.6                h7c369d9_2    conda-forge
h5py                      3.6.0           nompi_py310h00cbb18_100    conda-forge
harfbuzz                  4.2.0                hc1763ed_0    conda-forge
hdf4                      4.2.15               h0e5069d_3    conda-forge
hdf5                      1.12.1          nompi_h2a0e4a3_104    conda-forge
html5lib                  1.1                pyh9f0ad1d_0    conda-forge
icu                       69.1                 h0e60522_0    conda-forge
idna                      3.3                pyhd8ed1ab_0    conda-forge
imagesize                 1.3.0              pyhd8ed1ab_0    conda-forge
importlib-metadata        4.11.3          py310h5588dad_1    conda-forge
importlib_metadata        4.11.3               hd8ed1ab_1    conda-forge
importlib_resources       5.7.1              pyhd8ed1ab_0    conda-forge
iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
intel-openmp              2022.0.0          h57928b3_3663    conda-forge
ipykernel                 6.13.0          py310hbbfc1a7_0    conda-forge
ipython                   8.3.0           py310h5588dad_0    conda-forge
ipython_genutils          0.2.0                      py_1    conda-forge
ipywidgets                7.7.0              pyhd8ed1ab_0    conda-forge
jbig                      2.1               h8d14728_2003    conda-forge
jedi                      0.18.1          py310h5588dad_1    conda-forge
jinja2                    3.0.3              pyhd8ed1ab_0    conda-forge
jpeg                      9e                   h8ffe710_1    conda-forge
jsonschema                3.2.0              pyhd8ed1ab_3    conda-forge
jupyter                   1.0.0           py310h5588dad_7    conda-forge
jupyter-book              0.12.3             pyhd8ed1ab_0    conda-forge
jupyter-cache             0.4.3              pyhd8ed1ab_0    conda-forge
jupyter-server-mathjax    0.2.5              pyhc268e32_0    conda-forge
jupyter-sphinx            0.3.2              pyhd8ed1ab_1    conda-forge
jupyter_client            6.1.12             pyhd8ed1ab_0    conda-forge
jupyter_console           6.4.2              pyhd8ed1ab_0    conda-forge
jupyter_contrib_core      0.3.3                      py_2    conda-forge
jupyter_core              4.9.2           py310h5588dad_0    conda-forge
jupyter_nbextensions_configurator 0.4.1              pyhd8ed1ab_2    conda-forge
jupyter_server            1.13.5             pyhd8ed1ab_0    conda-forge
jupyterlab_widgets        1.1.0              pyhd8ed1ab_0    conda-forge
kiwisolver                1.4.2           py310h476a331_1    conda-forge
krb5                      1.19.3               h1176d77_0    conda-forge
latexcodec                2.0.1              pyh9f0ad1d_0    conda-forge
lcms2                     2.12                 h2a16943_0    conda-forge
lerc                      3.0                  h0e60522_0    conda-forge
libblas                   3.9.0              14_win64_mkl    conda-forge
libbrotlicommon           1.0.9                h8ffe710_7    conda-forge
libbrotlidec              1.0.9                h8ffe710_7    conda-forge
libbrotlienc              1.0.9                h8ffe710_7    conda-forge
libcblas                  3.9.0              14_win64_mkl    conda-forge
libclang                  13.0.1          default_h81446c8_0    conda-forge
libcurl                   7.83.0               h789b8ee_0    conda-forge
libdeflate                1.10                 h8ffe710_0    conda-forge
libffi                    3.4.2                h8ffe710_5    conda-forge
libgd                     2.3.3                h217ff3b_2    conda-forge
libglib                   2.70.2               h3be07f2_4    conda-forge
libgpuarray               0.7.6             h8ffe710_1003    conda-forge
libiconv                  1.16                 he774522_0    conda-forge
liblapack                 3.9.0              14_win64_mkl    conda-forge
libnetcdf                 4.8.1           nompi_h1cc8e9d_102    conda-forge
libpng                    1.6.37               h1d00b33_2    conda-forge
libsodium                 1.0.18               h8d14728_1    conda-forge
libssh2                   1.10.0               h680486a_2    conda-forge
libtiff                   4.3.0                hc4061b1_3    conda-forge
libwebp                   1.2.2                h57928b3_0    conda-forge
libwebp-base              1.2.2                h8ffe710_1    conda-forge
libxcb                    1.13              hcd874cb_1004    conda-forge
libxml2                   2.9.14               hf5bbc77_0    conda-forge
libxslt                   1.1.33               h34f844d_4    conda-forge
libzip                    1.8.0                hfed4ece_1    conda-forge
libzlib                   1.2.11            h8ffe710_1014    conda-forge
linkify-it-py             1.0.3              pyhd8ed1ab_0    conda-forge
lxml                      4.8.0           py310he2412df_3    conda-forge
lz4-c                     1.9.3                h8ffe710_1    conda-forge
m2w64-gcc-libgfortran     5.3.0                         6    conda-forge
m2w64-gcc-libs            5.3.0                         7    conda-forge
m2w64-gcc-libs-core       5.3.0                         7    conda-forge
m2w64-gmp                 6.1.0                         2    conda-forge
m2w64-libwinpthread-git   5.0.0.4634.697f757               2    conda-forge
mako                      1.2.0              pyhd8ed1ab_1    conda-forge
markdown-it-py            1.1.0              pyhd8ed1ab_0    conda-forge
markupsafe                2.1.1           py310he2412df_1    conda-forge
matplotlib                3.5.2           py310h5588dad_0    conda-forge
matplotlib-base           3.5.2           py310h79a7439_0    conda-forge
matplotlib-inline         0.1.3              pyhd8ed1ab_0    conda-forge
mdit-py-plugins           0.2.8              pyhd8ed1ab_0    conda-forge
mistune                   0.8.4           py310he2412df_1005    conda-forge
mkl                       2022.0.0           h0e2418a_796    conda-forge
mkl-service               2.4.0           py310h3d5ec83_0    conda-forge
msys2-conda-epoch         20160418                      1    conda-forge
munkres                   1.1.4              pyh9f0ad1d_0    conda-forge
myst-nb                   0.13.2             pyhd8ed1ab_0    conda-forge
myst-parser               0.15.2             pyhd8ed1ab_0    conda-forge
nbclient                  0.5.13             pyhd8ed1ab_0    conda-forge
nbconvert                 5.6.1              pyhd8ed1ab_2    conda-forge
nbdev                     1.1.23                     py_0    fastai
nbdime                    3.1.1              pyhd8ed1ab_0    conda-forge
nbformat                  5.4.0              pyhd8ed1ab_0    conda-forge
nbmake                    1.3.0              pyhd8ed1ab_1    conda-forge
nest-asyncio              1.5.5              pyhd8ed1ab_0    conda-forge
netcdf4                   1.5.8           nompi_py310h5489b47_101    conda-forge
notebook                  6.4.11             pyha770c72_0    conda-forge
numexpr                   2.7.3           py310hf5e1058_2    conda-forge
numpy                     1.22.3          py310hed7ac4c_2    conda-forge
openjpeg                  2.4.0                hb211442_1    conda-forge
openssl                   1.1.1o               h8ffe710_0    conda-forge
packaging                 21.3               pyhd8ed1ab_0    conda-forge
pandas                    1.4.2           py310hf5e1058_1    conda-forge
pandoc                    2.2.2                hde52d81_1    conda-forge
pandocfilters             1.5.0              pyhd8ed1ab_0    conda-forge
pango                     1.50.7               h66df5b2_0    conda-forge
parso                     0.8.3              pyhd8ed1ab_0    conda-forge
patsy                     0.5.2              pyhd8ed1ab_0    conda-forge
pcre                      8.45                 h0e60522_0    conda-forge
pickleshare               0.7.5                   py_1003    conda-forge
pillow                    9.1.0           py310h767b3fd_2    conda-forge
pip                       22.0.4             pyhd8ed1ab_0    conda-forge
pixman                    0.40.0               h8ffe710_0    conda-forge
pluggy                    1.0.0           py310h5588dad_3    conda-forge
prometheus_client         0.14.1             pyhd8ed1ab_0    conda-forge
prompt-toolkit            3.0.29             pyha770c72_0    conda-forge
prompt_toolkit            3.0.29               hd8ed1ab_0    conda-forge
psutil                    5.9.0           py310he2412df_1    conda-forge
pthread-stubs             0.4               hcd874cb_1001    conda-forge
pure_eval                 0.2.2              pyhd8ed1ab_0    conda-forge
py                        1.11.0             pyh6c4a22f_0    conda-forge
pybtex                    0.24.0             pyhd8ed1ab_2    conda-forge
pybtex-docutils           1.0.1           py310h5588dad_1    conda-forge
pycparser                 2.21               pyhd8ed1ab_0    conda-forge
pydantic                  1.9.0           py310he2412df_1    conda-forge
pydata-sphinx-theme       0.7.2              pyhd8ed1ab_0    conda-forge
pygments                  2.12.0             pyhd8ed1ab_0    conda-forge
pygpu                     0.7.6           py310h2873277_1003    conda-forge
pymc3                     3.9.3              pyhd8ed1ab_3    conda-forge
pyopenssl                 22.0.0             pyhd8ed1ab_0    conda-forge
pyparsing                 3.0.8              pyhd8ed1ab_0    conda-forge
pyqt                      5.12.3          py310h5588dad_8    conda-forge
pyqt-impl                 5.12.3          py310h8a704f9_8    conda-forge
pyqt5-sip                 4.19.18         py310h8a704f9_8    conda-forge
pyqtchart                 5.12            py310h8a704f9_8    conda-forge
pyqtwebengine             5.12.1          py310h8a704f9_8    conda-forge
pyrsistent                0.18.1          py310he2412df_1    conda-forge
pysocks                   1.7.1           py310h5588dad_5    conda-forge
pytables                  3.7.0           py310h5dd77a3_0    conda-forge
pytest                    7.1.2           py310h5588dad_0    conda-forge
python                    3.10.4          h9a09f29_0_cpython    conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
python-fastjsonschema     2.15.3             pyhd8ed1ab_0    conda-forge
python-graphviz           0.20               pyhaef67bd_0    conda-forge
python_abi                3.10                    2_cp310    conda-forge
pytz                      2022.1             pyhd8ed1ab_0    conda-forge
pywin32                   303             py310he2412df_0    conda-forge
pywinpty                  2.0.5           py310h00ffb61_1    conda-forge
pyyaml                    6.0             py310he2412df_4    conda-forge
pyzmq                     22.3.0          py310h73ada01_2    conda-forge
qt                        5.12.9               h556501e_6    conda-forge
qtconsole                 5.3.0              pyhd8ed1ab_0    conda-forge
qtconsole-base            5.3.0              pyhd8ed1ab_0    conda-forge
qtpy                      2.1.0              pyhd8ed1ab_0    conda-forge
requests                  2.27.1             pyhd8ed1ab_0    conda-forge
scipy                     1.8.0           py310h33db832_1    conda-forge
seaborn                   0.11.2               hd8ed1ab_0    conda-forge
seaborn-base              0.11.2             pyhd8ed1ab_0    conda-forge
send2trash                1.8.0              pyhd8ed1ab_0    conda-forge
setuptools                62.1.0          py310h5588dad_0    conda-forge
shellingham               1.4.0              pyh44b312d_0    conda-forge
six                       1.16.0             pyh6c4a22f_0    conda-forge
smmap                     3.0.5              pyh44b312d_0    conda-forge
snappy                    1.1.8                ha925a31_3    conda-forge
sniffio                   1.2.0           py310h5588dad_3    conda-forge
snowballstemmer           2.2.0              pyhd8ed1ab_0    conda-forge
soupsieve                 2.3.1              pyhd8ed1ab_0    conda-forge
sphinx                    4.5.0              pyh6c4a22f_0    conda-forge
sphinx-book-theme         0.1.9              pyhd8ed1ab_0    conda-forge
sphinx-comments           0.0.3              pyh9f0ad1d_0    conda-forge
sphinx-copybutton         0.5.0              pyhd8ed1ab_0    conda-forge
sphinx-external-toc       0.2.3              pyhd8ed1ab_0    conda-forge
sphinx-jupyterbook-latex  0.4.6              pyhd8ed1ab_0    conda-forge
sphinx-multitoc-numbering 0.1.3              pyhd8ed1ab_0    conda-forge
sphinx-panels             0.6.0              pyhd8ed1ab_0    conda-forge
sphinx-thebe              0.1.2              pyhd8ed1ab_0    conda-forge
sphinx-togglebutton       0.3.1              pyhd8ed1ab_0    conda-forge
sphinxcontrib-applehelp   1.0.2                      py_0    conda-forge
sphinxcontrib-bibtex      2.4.2              pyhd8ed1ab_0    conda-forge
sphinxcontrib-devhelp     1.0.2                      py_0    conda-forge
sphinxcontrib-htmlhelp    2.0.0              pyhd8ed1ab_0    conda-forge
sphinxcontrib-jsmath      1.0.1                      py_0    conda-forge
sphinxcontrib-qthelp      1.0.3                      py_0    conda-forge
sphinxcontrib-serializinghtml 1.1.5              pyhd8ed1ab_2    conda-forge
sqlalchemy                1.4.36          py310he2412df_0    conda-forge
sqlite                    3.38.3               h8ffe710_0    conda-forge
stack_data                0.2.0              pyhd8ed1ab_0    conda-forge
statsmodels               0.13.2          py310h2873277_0    conda-forge
tbb                       2021.5.0             h2d74725_1    conda-forge
terminado                 0.13.3          py310h5588dad_1    conda-forge
testpath                  0.6.0              pyhd8ed1ab_0    conda-forge
theano                    1.0.5           py310h8a704f9_3    conda-forge
tk                        8.6.12               h8ffe710_0    conda-forge
tomli                     2.0.1              pyhd8ed1ab_0    conda-forge
tornado                   6.1             py310he2412df_3    conda-forge
tqdm                      4.64.0             pyhd8ed1ab_0    conda-forge
traitlets                 5.1.1              pyhd8ed1ab_0    conda-forge
typing-extensions         3.10.0.2             hd8ed1ab_0    conda-forge
typing_extensions         3.10.0.2           pyha770c72_0    conda-forge
tzdata                    2022a                h191b570_0    conda-forge
uc-micro-py               1.0.1              pyhd8ed1ab_0    conda-forge
ucrt                      10.0.20348.0         h57928b3_0    conda-forge
unicodedata2              14.0.0          py310he2412df_1    conda-forge
urllib3                   1.26.9             pyhd8ed1ab_0    conda-forge
vc                        14.2                 hb210afc_6    conda-forge
vs2015_runtime            14.29.30037          h902a5da_6    conda-forge
vs2017_win-64             19.16.27033          hb90652a_6    conda-forge
vswhere                   3.0.2                h57928b3_0    conda-forge
wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
webencodings              0.5.1                      py_1    conda-forge
websocket-client          1.3.2              pyhd8ed1ab_0    conda-forge
wheel                     0.37.1             pyhd8ed1ab_0    conda-forge
widgetsnbextension        3.6.0           py310h5588dad_0    conda-forge
win_inet_pton             1.1.0           py310h5588dad_4    conda-forge
winpty                    0.4.3                         4    conda-forge
xarray                    2022.3.0           pyhd8ed1ab_0    conda-forge
xlrd                      2.0.1              pyhd8ed1ab_3    conda-forge
xorg-kbproto              1.0.7             hcd874cb_1002    conda-forge
xorg-libice               1.0.10               hcd874cb_0    conda-forge
xorg-libsm                1.2.3             hcd874cb_1000    conda-forge
xorg-libx11               1.7.2                hcd874cb_0    conda-forge
xorg-libxau               1.0.9                hcd874cb_0    conda-forge
xorg-libxdmcp             1.1.3                hcd874cb_0    conda-forge
xorg-libxext              1.3.4                hcd874cb_1    conda-forge
xorg-libxpm               3.5.13               hcd874cb_0    conda-forge
xorg-libxt                1.2.1                hcd874cb_2    conda-forge
xorg-xextproto            7.3.0             hcd874cb_1002    conda-forge
xorg-xproto               7.0.31            hcd874cb_1007    conda-forge
xz                        5.2.5                h62dcd97_1    conda-forge
yaml                      0.2.5                h8ffe710_2    conda-forge
zeromq                    4.3.4                h0e60522_1    conda-forge
zipp                      3.8.0              pyhd8ed1ab_0    conda-forge
zlib                      1.2.11            h8ffe710_1014    conda-forge
zstd                      1.5.2                h6255e5f_0    conda-forge

    Other ideas

    • Your environment file is quite large because it includes not only packages to run the example code but also packages to build the book. One idea would be to split this into 2 different files. This way readers of the book would have an easier time creating an environment to try out the code

    • I noticed that a previous version of your package is available from conda-forge: https://github.com/conda-forge/empyrical-dist-feedstock, but not the currrent version. I can add it to conda-forge if you like. That would save the time of installing from pip after the environment is solved

    opened by jdblischak 2
  • Chapter 6 Goblin Exercise

    Chapter 6 Goblin Exercise

    Hi,

    I'm working through the book atm, and I think I spotted a slight inaccuracy in the phrasing of the Goblin HP problem in Chapter 6 (Odds & Addends):

    The phrasing of the question is Suppose you are fighting a goblin and you have already inflicted 3 points of damage. What is your probability of defeating the goblin with your next successful attack?

    The solution subtracts a 1d6+3 distribution from a 2d6 distribution and looks at the likelihood of the remainder being < 0, which returns 0.5. However, given the phrasing you have already inflicted 3 points of damage and what is probability of defeating the goblin with your *next* successful attack?, am I right in assuming that the initial hit point distribution is necessarily greater than 3 (since the goblin is apparently still standing)?

    Assuming I'm correct, would the hp distribution be more accurately described by

    hp = make_die(6).add_dist(make_die(6))
hypos = hp.qs
impossible = hypos <= 3
hp.ps[impossible] = 0
hp.normalize()

    Alternatively, phrased as likelihood over the whole array of hypos:

    #prior hp
hp = make_die(6).add_dist(make_die(6))
hypos = hp.qs
#likelihood of the goblin standing after 3 dmg:
likelihood = [hypo > 3 for hypo in hypos]
#updating prior given data:
hp *= likelihood
hp.normalize()

    This lead me to the following solution

    damage = make_die(6).add_dist(3)
remainder = hp.sub_dist(damage)
remainder.le_dist(0)

    which returns 0.45454545 (as opposed to 0.5 in the original phrasing of the solution)

    Please let me know if I'm barking up the wrong tree here! It's possible I overthought things, but figured I'd post this here.

    opened by cjungerius 2
  • Small typo in solutions to exercise 3 in ch2

    Small typo in solutions to exercise 3 in ch2

    Hi Allen,

    Thanks for this fantastic resource, it's helping me a lot!

    I noticed a small typo in the solutions to the 3rd exercise in chapter 2

    If the car is behind Door 1, Monty would have opened Door 2 If the car is behind Door 2, Monty would always Door 3 If the car is behind Door 3, Monty would have opened Door 3

    If the car is behind Door 1, Monty would have opened Door 2 If the car is behind Door 2, Monty would always open Door 3 If the car is behind Door 3, Monty would have opened Door 2

    If you agree with the suggestion I will be happy to open a PR fixing it.

    Have a nice day

    opened by nablabits 2
  • Typo in Theorem 1 from Laws of probability

    Typo in Theorem 1 from Laws of probability

    Thanks for your amazing books dear Allen. I found this typo by reading the early version at O'Reilly site (multiplying instead of dividing over P(B)): IMG_20210410_155600

    opened by GermanCM 2
  • The correctness of EvalExponentialPdf method in thinkbayes2.py

    The correctness of EvalExponentialPdf method in thinkbayes2.py

    Dear professor Downey,

    In chapter 7.6, you would like to compute the time between goals and the code in EvalExponentialPdf method is:

    return lam*math.exp(-lam*x)

    but I think the probability between two events is:

    return math.exp(-lam*x)

    The first term lam variable should be dropped.

    Here comes my justification: Assume T is the time interval between two goals. The Pr(T > x ) means the probability in a certain time interval x should not occur scoring. so we take the k=0 to the Poisson distribution, then we got math.exp(-lam*x).

    Although I know the EvalExponentialPdf only be called by MakeExponentialPmf, and the lam variable will be canceled out after Normalize function. so the result stays the same. But I still want to confirm with you the correctness of the method. Thanks in advance!

    opened by 18fanfan 2
  • fix Anaconda environment name

    fix Anaconda environment name

    The environment.yml file install the dependencies in the 'base' environment. However this environment already exists and is the Anaconda default. It causes an error when trying to follow the installation steps in the README.md file.

    The README offer to activate the environment with the name ThinkBayes2. Use this name instead in the yml file.

    opened by michaelmera 2
  • Is the solution for cookie problem without replacement (cookie3.py) incomplete?

    Is the solution for cookie problem without replacement (cookie3.py) incomplete?

    Hi!

    First, let me thank you for creating all this, it is really a nice resource for developers like me to learn how to solve problems with bayesian methods. Now the my question: I was surprised by the output of the code on cookie3.py. Am I right to think the code in that file is an incomplete solution to the "cookie problem without replacement"? If it were a complete solution I would have expected the code to construct a growing set of hypothesis as cookies go away, to consider the scenarios in which the cookie removal could be evening out between the bowls, which the current code is not doing.

    Say for example, that you eat 21 vanilla cookies ( no replacement ;-) )

        for x in range(0, 21):
         suite.Update('vanilla')

    Then the cookie3.py will printout:

    Hist({'vanilla': 9, 'chocolate': 10}) 1.0 Hist({'vanilla': 0, 'chocolate': 20}) 0.0

    Reaching the conclusion that there in no chance (0.0) the 21th cookie came from bowl2, when it could have been the case that 10 of those cookies came from "bow1" and 11 from "bowl2" making the probability of getting a 21th vanilla cookie from bowl2 non zero. Or maybe I missed some simplifying assumption in the text of the book?

    Thanks

    opened by luxspes 2
  • Install empiricaldist from conda-forge

    Install empiricaldist from conda-forge

    tl;dr Install empiricaldist with conda now that it is available from conda-forge

    Background

    A minimum of 0.6.7 is required because version 0.6.6 is broken (it doesn't import matplotlib, which it requires for its plotting methods)

    https://github.com/conda-forge/empiricaldist-feedstock/pull/1 https://anaconda.org/conda-forge/empiricaldist/files?version=0.6.7

    Testing

    I confirmed that I could build all the main chapter notebooks with this updated environment:

    mamba env create --file environment.yml
conda activate ThinkBayes2

for nb in chap*.ipynb
do
  jupyter execute $nb
done

    I also attempted to build the non-chapter notebook, but clustering.ipynb fails because the environment doesn't have scikit-learn installed:

    ModuleNotFoundError: No module named 'sklearn'

    Given how it already takes so long to solve and build the conda environment, and also that the clustering notebook is not included in the online version of the book, I decided to not add it in this PR.

    Other

    Also note that per our discussion in https://github.com/AllenDowney/ThinkBayes2/pull/56#issuecomment-1117536918, I took a stab at creating two separate environments: one for readers, and one for you to build the book. I loss motivation for now because:

    1. The reader-only env wasn't that light-weight anyways. The final few chapters ended up requiring a bunch of packages that I had hoped to remove
    2. I ran into the frustrating pymc3/arviz error that you had previously run into. Pinning to old versions of these dependencies limits the long-term usefulness of the book's code (eg it can't benefit from bug fixes in later releases). When I tried to force conda to install newer versions on macOS, the conflict was due to some low-level package (I can't remember which), which would be non-trivial to fix or workaround
    3. I got errors like missing scikit-learn in clustering.ipynb (as mentioned above). It's unclear to me which code is expected to be able to be executed using the conda env

    In case it could be useful to you, here's what I had when I stopped:

    https://github.com/jdblischak/ThinkBayes2/tree/more-conda-updates https://github.com/jdblischak/ThinkBayes2/blob/more-conda-updates/environment.yml https://github.com/jdblischak/ThinkBayes2/blob/more-conda-updates/environment-book.yml

    opened by jdblischak 1
  • Linda problem should use conditional probability when it comes to sex.

    Linda problem should use conditional probability when it comes to sex.

    One thing I feel the book misses multiple times in chapter I is the fact, that Linda's sex is given in the problem description (I picked an exercise as a sample):

    Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations. Which is more probable?

    Linda is a banker.

Linda is a banker and considers herself a liberal Democrat

    According to sample solution to this exercise, we should answer "Linda is a banker" with P(female & banker). However, given it is clear from the description (from the usage of "she"), that Linda is a female, this fact should be considered given, and the solution should be P(banker | female).

    Of course, this fact doesn't change the answer (being a female banker is still more probable than being a liberal, democrat, female banker), however I feel like at least the answer to the above exercise needs to be changed, or the fact that multiple (In my humble opinion - valid) approaches are possible here.

    opened by Szustarol 0
  • Lincoln Index - Three parameter model

    Lincoln Index - Three parameter model

    I am receiving the following error when I attempt to compute the likelihood in the three parameter model: for N, p0, p1 in joint3_pmf.index:

    ValueError: not enough values to unpack (expected 3, got 2)

    I think the issue is that when I attempt to use make_joint using p1 and the joint2_pmf, I do not get the triplet, but rather only the pair, mean p1 is not attaching and thus the multi-index only has 2 items instead of three.

    Do you have any suggestions about how best to fix?

    Thank you

    opened by zjachc 17
  • Oliver Problem - Chapter 6 - General Question

    Oliver Problem - Chapter 6 - General Question

    Brief Background

    Working through this excellent book with a colleague and he noticed something interesting on the Oliver's Blood problem.

    Original solution, assuming 60% of local pop have type O and 1% have type AB:

    like1 = 0.01
like2 = 2 * 0.6 * 0.01

likelihood_ratio = like1 / like2
post_odds = 1 * like1 / like2
prob(post_odds) # 0.454545

    The interesting point mentioned by my colleague was this:

    • we could try the problem with a range of different values of % of local population having type AB and still get the same final probability:
    import numpy as np

def prob(o):
    return o / (o+1)
  
  
o = 0.6 # we fix type o proportion in local pop

# iterate over various ab vals
for ab in np.linspace(0.02, 0.40, 20):
  like1 = ab
  like2 = 2 * o * ab

  assert round(prob(like1 / like2),4) == 0.4545

    Clearly the ab cancel one another out in each likelihood calculation.

    Curiosity

    My interpretation of this is that the information on percent of local population with type AB does not matter for this problem. Is there additional insight or context you could add to this? My colleague & I were both a bit surprised, but the math is clear enough.

    Another thought: Perhaps we have to assume the possibility that the proportion of O would shift as AB changes, which would indeed change the final probabilities (even though there are more blood types than O and AB)

    • As AB becomes more likely, and O becomes less likely, then the evidence is increased that Oliver is one of the people that left blood (in this problem).

    Again, no bug found - just curious about any additional commentary on the above. We are very much enjoying the book and exercises.

    opened by davidwanner-8451 0
  • gss_bayes.csv

    gss_bayes.csv

    Should gss_bayes.csv (Chapter 1) be in /data ?

    (found it at https://raw.githubusercontent.com/AllenDowney/BiteSizeBayes/master/gss_bayes.csv after opening the notebook BTW)

    opened by rcsmit 0
  • What is considered to be the

    What is considered to be the "data" in the M&M solution? (Ch 2: Bayes's Theorem)

    The problem hint indicates the trick is defining the hypotheses and data carefully:

    Hint: The trick to this question is to define the hypotheses and the data carefully.

    The solution clearly states the "hypotheses":

    # Hypotheses:
# A: yellow from 94, green from 96
# B: yellow from 96, green from 94

    What is the "data" in this solution?

    My guess is the data is the color mixes in the bags (e.g. 1994 bag has 30% brown, etc. 1996 bag has 24% blue, etc.).

    With this data definition, the first likelihood would be represented by "the probability of the color mix given that yellow is from 94 and green is from 96". It's not clear to me why that probability would be represented by 0.2*0.2. Maybe I have to think more or maybe I've guessed wrong about the data definition.

    opened by rigdern 1
  • Example 6-8 number of Outperforming Portfolios

    Example 6-8 number of Outperforming Portfolios

    First of all thank you for writing this book. It's been an informative read and the examples are fun and challenging.

    That being said I believe there is a typo in Example 6-8: 312 outperforming portfolios must mean there are 430.5 honest members of congress. It is quite funny to say that there is a half-honest politician, but 313 outperforming portfolios does result in a whole number of honest and dishonest members of congress who add up to 538. So it makes more sense if the number of outperforming portfolios is 313.

    opened by Joe-Morgan-Code 1
Owner
Allen Downey
Professor at Olin College, author of Think Python, Think Bayes, Think Stats, and other books. Blog author of Probably Overthinking It.
Allen Downey
GitHub http://allendowney.github.io/ThinkBayes2/
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