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MIT Probabilistic Computing Project's Projects

adev icon adev

Haskell prototype to accompany the paper "ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs"

autoimcmc icon autoimcmc

Code accompanying the paper "Automating Involutive MCMC using Probabilistic and Differentiable Programming"

bayesdb icon bayesdb

A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself. New implementation in http://github.com/probcomp/bayeslite

bayeslite icon bayeslite

BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.

bdbcontrib icon bdbcontrib

BayesDB contributions, including plotting, helper methods, and examples

caliban icon caliban

Research workflows made easy, locally and in the Cloud.

cgpm icon cgpm

Library of composable generative population models which serve as the modeling and inference backend of BayesDB.

cgpm2 icon cgpm2

Minimal implementation of composable generative population models for Bayesian synthesis of probabilistic programs.

clips.jl icon clips.jl

Cooperative Language-Guided Inverse Plan Search (CLIPS).

cloudless icon cloudless

Distributed computational science made easy, in Python

crosscat icon crosscat

A domain-general, Bayesian method for analyzing high-dimensional data tables

curve-fitting icon curve-fitting

A simple application demonstrating some of the capabilities of the Metaprob probabilistic programming language

depthrender icon depthrender

A depth renderer with a simple numpy interface, and a gRPC microservice that serves it.

distributions icon distributions

Low-level primitives for collapsed Gibbs sampling in python and C++

durablevs icon durablevs

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model

dynamicforwarddiff.jl icon dynamicforwarddiff.jl

An experimental fork of ForwardDiff.jl to support differentiation with respect to an a-priori unknown number of parameters

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