Interface between Turing.jl and MonteCarloMeasurements.jl
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Updated
Apr 17, 2023 - Julia
Interface between Turing.jl and MonteCarloMeasurements.jl
6D pose estimation using sampling-based Bayesian inference algorithms. Produced the results of Tim Redick's dissertation.
🎲 Probabilistic Programming eXecution protocol (PPX)
A Bayesian approach to simulate the United Rugby Championship
Make Julia code probabilistic-programming-ready by allowing calls to `rand` to be annotated with traced addresses.
Sparse Signaling Pathway Sampling: MCMC for signaling pathway inference
Gen plugin to allow PyTorch computations to be used as Gen generative functions.
A demonstration of performing Extreme Value Theory (EVT) using the Block Maxima method with Bayesian sampling in Julia.
Educational implementation of a probabilistic programming language in Julia
Simple implementation of probabilistic programs for the Julia programming language
Type stable implementation of a Bayesian network.
Automatically convert Julia methods to Gen functions.
WIP successor to Soss.jl
SossMLJ makes it easy to build MLJ machines from user-defined models from the Soss probabilistic programming language
Building blocks for simple and advanced particle filtering in Gen.
Automatic probabilistic programming for scientific machine learning and dynamical models
Implementations of parallel tempering algorithms to augment samplers with tempering capabilities
Common types and interfaces for probabilistic programming
Preheat your MCMC
Kernel Density Estimate with product approximation using multiscale Gibbs sampling
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