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This aims to be a collection of tools for performing Bayesian parameter estimation and model selection on stochastic processes. The immediate goal is to implement estimation methods
Use pandas data frame to load the diffusion process time series data from files of different types. Initially, only CSV files are needed.
Write functions for computing exact posterior distributions for Wiener and Ornstein-Uhlenbeck processes. There must be options for providing different prior distributions for the parameters.
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