Mixture models monte carlo bayesian updating and dynamic models


25-Nov-2017 16:14

Therefore, the overview is divided into two parts: on-line filtering and batch filtering/smoothing.Some of these algorithms are general algorithms for approximate Bayesian inference and others are specialized for dynamic models.However, it does allow the state/measurement equations, conditional on the discrete state, to be non-Gaussian.Represent the state posterior by a large set of samples drawn from the distribution.For this, one could use Monte Carlo, quadrature, or Laplace's method.

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This can be understood as having discrete state variables in addition to the continuous ones.The measurement equation is converted into a linear-Gaussian equation by regressing the observation onto the state.