52 Repositories
Python population-monte-carlo Libraries
Locally cache assets that are normally streamed in POPULATION: ONE
Population One Localizer This is no longer needed as of the build shipped on 03/03/22, thank you bigbox :) Locally cache assets that are normally stre
PyElecCL - Electron Monte Carlo Second Checks
PyElecCL Python program to perform second checks for electron Monte Carlo radiat
Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX
mini-hmc-jax This is a simple implementation of Hamiltonian Monte Carlo in JAX t
Predict the income for each percentile of the population (Python) - FRENCH
05.income-prediction Predict the income for each percentile of the population (Python) - FRENCH Effectuez une prédiction de revenus Prérequis Pour ce
Learned model to estimate number of distinct values (NDV) of a population using a small sample.
Learned NDV estimator Learned model to estimate number of distinct values (NDV) of a population using a small sample. The model approximates the maxim
PolytopeSampler is a Matlab implementation of constrained Riemannian Hamiltonian Monte Carlo for sampling from high dimensional disributions on polytopes
PolytopeSampler PolytopeSampler is a Matlab implementation of constrained Riemannian Hamiltonian Monte Carlo for sampling from high dimensional disrib
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch
This computer program provides a reference implementation of Lagrangian Monte Carlo in metric induced by the Monge patch. The code was prepared to the final version of the accepted manuscript in AISTATS and is provided as-is.
This repository provides some codes to demonstrate several variants of Markov-Chain-Monte-Carlo (MCMC) Algorithms.
Demo-of-MCMC These files are based on the class materials of AEROSP 567 taught by Prof. Alex Gorodetsky at University of Michigan. Author: Hung-Hsiang
smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al
smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in data science, Machine Learning, and scientific inference, with the design goal of unifying the automation (of Monte Carlo simulations), user-friendliness (of the library), accessibility (from multiple programming environments), high-performance (at runtime), and scalability (across many parallel processors).
High dimensional black-box optimizer using Latent Action Monte Carlo Tree Search algorithm
LA-MCTS The code is based of paper Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. Component LA-MCTS has thr
A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
DeepKE is a knowledge extraction toolkit supporting low-resource and document-level scenarios for entity, relation and attribute extraction. We provide comprehensive documents, Google Colab tutorials, and online demo for beginners.
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.
CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,
Clustering with variational Bayes and population Monte Carlo
pypmc pypmc is a python package focusing on adaptive importance sampling. It can be used for integration and sampling from a user-defined target densi
Pythonic particle-based (super-droplet) warm-rain/aqueous-chemistry cloud microphysics package with box, parcel & 1D/2D prescribed-flow examples in Python, Julia and Matlab
PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo
Simulate genealogical trees and genomic sequence data using population genetic models
msprime msprime is a population genetics simulator based on tskit. Msprime can simulate random ancestral histories for a sample of individuals (consis
Generating synthetic mobility data for a realistic population with RNNs to improve utility and privacy
lbs-data Motivation Location data is collected from the public by private firms via mobile devices. Can this data also be used to serve the public goo
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.
CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning This is the code for implementing the MADDPG algorithm presented in
Monitor the stability of a pandas or spark dataframe ⚙︎
Population Shift Monitoring popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets.
Machine learning algorithms for many-body quantum systems
NetKet NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and
A GUI to automatically create a TOPAS-readable MLC simulation file
Python script to create a TOPAS-readable simulation file descriring a Multi-Leaf-Collimator. Builds the MLC using the data from a 3D .stl file.
Python script to automate the plotting and analysis of percentage depth dose and dose profile simulations in TOPAS.
topas-create-graphs A script to automatically plot the results of a topas simulation Works for percentage depth dose (pdd) and dose profiles (dp). Dep
Python script to combine the statistical results of a TOPAS simulation that was split up into multiple batches.
topas-merge-simulations Python script to combine the statistical results of a TOPAS simulation that was split up into multiple batches At the top of t
Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo"
dblmahmc Code to go with the paper "Decentralized Bayesian Learning with Metropolis-Adjusted Hamiltonian Monte Carlo" Requirements: https://github.com
The official implementation of the Hybrid Self-Attention NEAT algorithm
PUREPLES - Pure Python Library for ES-HyperNEAT About This is a library of evolutionary algorithms with a focus on neuroevolution, implemented in pure
A simple Monte Carlo simulation using Python and matplotlib library
Monte Carlo python simulation Install linux dependencies sudo apt update sudo apt install build-essential \ software-properties-commo
An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects different compression algorithms have.
ImageCompressionSimulation An Image compression simulator that uses Source Extractor and Monte Carlo methods to examine the post compressive effects o
Massively parallel Monte Carlo diffusion MR simulator written in Python.
Disimpy Disimpy is a Python package for generating simulated diffusion-weighted MR signals that can be useful in the development and validation of dat
Custom Python code for calculating the Probability of Profit (POP) for options trading strategies using Monte Carlo Simulations.
Custom Python code for calculating the Probability of Profit (POP) for options trading strategies using Monte Carlo Simulations.
Application of the L2HMC algorithm to simulations in lattice QCD.
l2hmc-qcd 📊 Slides Recent talk on Training Topological Samplers for Lattice Gauge Theory from the Machine Learning for High Energy Physics, on and of
Annealed Flow Transport Monte Carlo
Annealed Flow Transport Monte Carlo Open source implementation accompanying ICML 2021 paper by Michael Arbel*, Alexander G. D. G. Matthews* and Arnaud
Probabilistic Tensor Decomposition of Neural Population Spiking Activity
Probabilistic Tensor Decomposition of Neural Population Spiking Activity Matlab (recommended) and Python (in developement) implementations of Soulat e
Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".
A Flow-based latent state generative model of neural population responses to natural images Code for "A Flow-based latent state generative model of ne
Resilience from Diversity: Population-based approach to harden models against adversarial attacks
Resilience from Diversity: Population-based approach to harden models against adversarial attacks Requirements To install requirements: pip install -r
HSPICE can not perform Monte Carlo (MC) simulations while considering aging effects
HSPICE can not perform Monte Carlo (MC) simulations while considering aging effects. I developed a python wrapper that automatically performs MC and aging simulations using HPSICE to save engineering hours.
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
Alpha Zero General (any game, any framework!) A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play
Powerful, efficient particle trajectory analysis in scientific Python.
freud Overview The freud Python library provides a simple, flexible, powerful set of tools for analyzing trajectories obtained from molecular dynamics
China and India Population and GDP Visualization
China and India Population and GDP Visualization Historical Population Comparison between India and China This graph shows the population data of Indi
Dropdown population implementation for Django REST Framework
drf-dropdown Dropdown population implementation for Django REST Framework Usage Add DropdownView to API URL # urls.py import dropdown urlpatterns = [
Hypernets: A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
A General Automated Machine Learning framework to simplify the development of End-to-end AutoML toolkits in specific domains.
Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations.
Elicited Helper tools to construct probability distributions built from expert elicited data for use in monte carlo simulations. Credit to Brett Hoove
Monte Carlo simulation of 3G rules
mc3g Monte Carlo simulation of 3G rules This project contains the Python code to do simulations of events according to the 3G rule (in German: "Geimpf
Code for the Population-Based Bandits Algorithm, presented at NeurIPS 2020.
Population-Based Bandits (PB2) Code for the Population-Based Bandits (PB2) Algorithm, from the paper Provably Efficient Online Hyperparameter Optimiza
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f
Smoking Simulation is an app to simulate the spreading of smokers and non-smokers, their interactions and population during certain amount of time.
Smoking Simulation is an app to simulate the spreading of smokers and non-smokers, their interactions and population during certain
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021
NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree
A parallel framework for population-based multi-agent reinforcement learning.
MALib: A parallel framework for population-based multi-agent reinforcement learning MALib is a parallel framework of population-based learning nested
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Overlap-based 3D LiDAR Monte Carlo Localization This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D
Decipher using Markov Chain Monte Carlo
Decipher using Markov Chain Monte Carlo
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
AdvancedHMC.jl AdvancedHMC.jl provides a robust, modular and efficient implementation of advanced HMC algorithms. An illustrative example for Advanced
Gaussian processes in TensorFlow
Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow