Causal inference, graphical models and structure learning in Julia

A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.

Sleek implementations of the ZigZag, Boomerang and other assorted piecewise deterministic Markov processes for Markov Chain Monte Carlo including Sticky PDMPs for variable selection

Flexible filtering and smoothing in Julia

Automatic probabilistic programming for scientific machine learning and dynamical models

Iterators with message passing and feedback loops

Generalized golden sequences, a form of low discrepancy sequence or quasi random numbers

Gaussian distributions as state variables and for uncertainty quantification with Unitful

Backward-filtering forward-guiding with StochasticDiffEq.jl

Nonparametric Bayesian volatility learning under microstructure noise

Statistical inference for Poisson Processes

++ incorporated into https://github.com/JuliaLang/IterativeSolvers.jl ++ Induced Dimension Reduction method IDR(s) for solving general non-symmetric linear equations using a Krylov method, for example ordinary linear equations or Sylvester and Stein equations.

Maps from the line into spaces

Image analysis and stochastic processes on shape and landmark spaces.

Code accompanying the paper Frank van der Meulen, Moritz Schauer: Bayesian estimation of discretely observed multi-dimensional diffusion processes using guided proposals

This package is obsoleted by https://github.com/mschauer/Bridge.jl

The persistent data structure Finger Tree in Julia