A general-purpose probabilistic programming system with programmable inference
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Updated
May 9, 2024 - Julia
A general-purpose probabilistic programming system with programmable inference
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Bayesian Segmentation of Spatial Transcriptomics Data
A common framework for implementing and using log densities for inference.
Kernel Density Estimate with product approximation using multiscale Gibbs sampling
Markov Chain Monte Carlo convergence diagnostics in Julia
Graphical tools for Bayesian inference and posterior predictive checks.
Bayesian Information Gap Decision Theory
Is there anything we can't make Bayesian?
SMARTboost (boosting of smooth symmetric regression trees)
Bayesian gene tree reconciliation and WGD inference using amalgamated likelihood estimation
A set of tutorials for building likelihood based models in ACT-R
A Julia package for bayesian probabilistic matrix factorization (BPMF).
Implementations for some distributions using a consistent API and AD-friendly code.
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