628 Open source projects that are alternatives of or similar to models

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

Fast & scalable MCMC for all your exoplanet needs!

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

A high-level Bayesian analysis API written in Python

rstanarm R package for Bayesian applied regression modeling

A python module to detect stellar flares using Bayesian model comparison

GPstuff - Gaussian process models for Bayesian analysis

Fast and Easy Infinite Neural Networks in Python

Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB

Expressions and dataframes with statistical details 📉 📜🔣✅

Full Bayesian Inference for Hidden Markov Models

Sequential Monte Carlo algorithm for approximation of posterior distributions.

Gaussian processes in TensorFlow

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

A collection of Bayesian data analysis recipes using PyMC3

A Bayesian Analysis Toolkit in Julia

ParaMonte: Plain Powerful Parallel Monte Carlo and MCMC Library for Python, MATLAB, Fortran, C++, C.

Statistical Rethinking with PyTorch and Pyro

PyMC3 educational resources

Probabilistic Programming with Gaussian processes in Julia

Probabilistic programming via source rewriting

Useful tools for periodicity analysis in time series data.

Fast and flexible Gaussian Process regression in Python

Scalable 1D Gaussian Processes in C++, Python, and Julia

Separating Normalizing Flows code from Pyro and improving API

Simultaneous Localization and Mapping(SLAM) also gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features.

Transformations to contrained variables from ℝⁿ.

🎓 Uni-Bonn Decision Analysis graduate course, lectures and materials

Material for AMLD 2020 workshop "Bayesian Inference: embracing uncertainty"

Bayesian spatial analysis

RStan, the R interface to Stan

How to do Bayesian statistical modelling using numpy and PyMC3

Rank-normalization, folding, and localization: An improved R-hat for assessing convergence of MCMC

Blang's software development kit

Bayesian inference with probabilistic programming.

C++ template library for probabilistic programming

A Python package for building Bayesian models with TensorFlow or PyTorch

This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.

Material for a Bayesian statistics workshop

Probabilistic Programming with Gaussian processes in Julia

Machine learning, in numpy

A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation

shinystan R package and ShinyStan GUI

R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

Particle filtering and sequential parameter inference in Python

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

The Trendiness of Trends

Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html

Statistical Rethinking (2nd Ed) with Tensorflow Probability

A Python package for approximate Bayesian inference and optimization using Gaussian processes

A didactic Gaussian process package for researchers in Jax.

Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)

[Implementation example] Attend and Diagnose: Clinical Time Series Analysis Using Attention Models

Python library for Bayesian hyper-parameters optimization

Time series competition data

📈 PyTorch dual-attention LSTM-autoencoder for multivariate Time Series 📈

Barrage is an opinionated supervised deep learning tool built on top of TensorFlow 2.x designed to standardize and orchestrate the training and scoring of complicated models.

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

Probabilistic question-asking system: the program asks, the users answer. The minimal goal of the program is to identify what the user needs (a target), even if the user is not aware of the existence of such a thing/product/service.