Sentence examples for full posterior probability from inspiring English sources

Bayesian particle filters Particle filter methods utilise Monte-Carlo Markov chain (MCMC) to approximate the conditional posterior probability assignment p(x t |yt−1) or the full posterior probability p(x t |yt−1).

These points of concordance between human and model TOJ performance suggest that the brain indeed integrates across the full posterior probability distribution.

First, the full posterior probability is estimated by the partial posterior probability.

Despite the simplicity of ABC, open questions remain regarding to what extent ABC achieves its goal of approximating the full posterior probability.

A few recent papers have considered summary statistic selection from the viewpoint of aiming for better inference or better approximation to the full posterior probability [ 18– 218.

Reference [ 20] also aimed for better estimation of θ and also used auxiliary simulations, but, unlike [ 19], summary statistics were pursued that minimized the entropy (uncertainty) of the estimated full posterior probability.

The Bayesian approach allows for full posterior probabilities and capture of the uncertainty in the estimates.

Full posterior probabilities of all alternative models for all test genes are included in Additional file 2. This set includes all genes in the data that are not classified as weakly expressed according to the criterion explained previously [ 20].

By exploring the full posterior probabilities and retaining the probabilities of models of different sizes, we can employ Bayesian model averaging (Hoeting et al., 1999), thus incorporating model uncertainty and not conditioning our inferences to the selection of a particular model.

Unlike former solutions, the paper proposes an algorithm that provides a full (approximate) posterior probability density function (pdf) of unknown parameters.

The full true posterior probability of the model is then given by: P (y, s, Θ ) = P (s 0 | π 0 ) ∏ t T P (s t | s t − 1, π t ) P (y t | s t, θ ) P (π t ) P (π 0 ) P where, P(π t ), P(π 0 ) and P are chosen to be non-informative priors.