MCMC Inference for a Hawkes process in Julia
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Implementations of Infinitesimal Continuous Normalizing Flows Algorithms in Julia
Network Hawkes processes in Julia.
Fast inference for Gaussian processes in problems involving time. Partly built on results from ht...
Graphical tools for Bayesian inference and posterior predictive checks
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Distributed MCMC Inference in Dirichlet Process Mixture Models (High Performance Machine Learning...
Bayesian Generalized Linear models using `@formula` syntax.
Examples for Bayesian inference using DynamicHMC.jl and related packages.
Kernel Density Estimate with product approximation using multiscale Gibbs sampling
A statistical toolbox for diffusion processes and stochastic differential equations. Named after ...
Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesia...
A hybrid sampler for the Pólya-Gamma distribution in Julia, implementing multiple algorithms for ...
Probabilistic programming via source rewriting
Robust, Fast, and Parallel Global Sensitivity Analysis (GSA) in Julia
A unified interface for simulating and evaluating sequential sampling models in Julia.