: The posterior; the probability of the hypothesis (e.g. that a parameter has a certain value) given the data: The likelihood of observing/generating the data given the hypothesis: The prior ...

This repository contains a tutorial on how to use hierarchical coarse-grained models and mutli-level Bayesian optimization for molecular discovery. Although the example system is quite simple, the ...

Abstract: A nonlinear Bayesian filter is proposed in this paper for a general nonlinear system of continuous time dynamics and discrete time measurements. In this filter, a transient Fokker-Planck ...

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical ...

Approximate Bayesian computation (ABC) algorithms are a class of Monte Carlo methods for doing inference when the likelihood function can be simulated from, but not explicitly evaluated. This ...

We suspect that you had more than enough mathematics in the form of Bayes Theorem last week so this week we’ll explain how it’s used in what is called Bayesian filtering to remove spam (note that the ...