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

Causal inference, graphical models and structure learning with the PC algorithm.

Flexible filtering and smoothing in Julia

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

Automatic probabilistic programming for scientific machine learning and dynamical models

Iterators with message passing and feedback loops

Gaussian distributions as state variables and for uncertainty quantification with Unitful

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

Backward-filtering forward-guiding with StochasticDiffEq.jl

++ 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

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

Image analysis and stochastic processes on shape and landmark spaces.

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

Statistical inference for Poisson Processes

Nonparametric Bayesian volatility learning under microstructure noise

The persistent data structure Finger Tree in Julia