212 Open source projects that are alternatives of or similar to flowtorch-old

Blang's software development kit

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Bayesian inference with probabilistic programming.

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Forecasting 🇫🇷 elections with Bayesian statistics 🥳

A collection of Bayesian data analysis recipes using PyMC3

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RStan, the R interface to Stan

Bayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available

Transformations to contrained variables from ℝⁿ.

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Probabilistic programming via source rewriting

BOPP: Bayesian Optimization for Probabilistic Programs

Bayesian Evolutionary Analysis by Sampling Trees

Personal project to compare hierarchical linear regression in PyMC3 and PyStan, as presented at http://pydata.org/london2016/schedule/presentation/30/ video: https://www.youtube.com/watch?v=Jb9eklfbDyg

Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

Machine learning, in numpy

GPstuff - Gaussian process models for Bayesian analysis

Fast and Easy Infinite Neural Networks in Python

A qualitative probabilistic programming language based on ranking theory

Bayes by Backprop implemented in a CNN in PyTorch

Probabilistic Programming with Gaussian processes in Julia

ELFI - Engine for Likelihood-Free Inference

Scalable inference for a generative model of astronomical images

This project has moved to https://git.dynare.org/Dynare/dynare

Infer.NET is a framework for running Bayesian inference in graphical models

This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis

The base NIMBLE package for R

Implementation of Bayesian Recurrent Neural Networks by Fortunato et. al

Sequential Monte Carlo in python

Modular Probabilistic Programming on MXNet

PyMC3 codes of Lee and Wagenmakers' Bayesian Cognitive Modeling - A Pratical Course

MrBayes is a program for Bayesian inference and model choice across a wide range of phylogenetic and evolutionary models. For documentation and downloading the program, please see the home page:

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

Companion package for the Coursera Statistics with R specialization

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