Statistics and stochastic calculus for Markov processes in continuous time, include univariate and multivariate stochastic processes such as stochastic differential equations or diffusions (SDE's) or Levy processes.
Features
- Define and simulate diffusion processes in one or more dimension
- Continuous and discrete likelihood using Girsanovs theorem and transition densities
- Monte Carlo sample diffusion bridges, diffusion processes conditioned to hit a point v at a prescribed time T
- Brownian motion in one and more dimensions
- Ornstein-Uhlenbeck processes and Ornstein-Uhlenbeck bridges
- Bessel processes
Project Samples
License
MIT LicenseFollow Bridge.jl
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