Sentence examples for explore the posterior from inspiring English sources
A Markov Chain Monte Carlo (MCMC) method is used to explore the posterior density of model parameters using measured data from dynamic tests at different temperatures.
Several adaptive Markov Chain Monte Carlo (MCMC) sampling techniques are investigated and implemented to explore the posterior probability density functions.
We make use of this technique to explore the posterior trajectories of inferred bacterial growth curves following analysis.
In such cases, sampling methods offer a way to explore the posterior distribution via an arbitrarily large sample drawn from it without requiring any further knowledge.
However, popular algorithms such as the well known random-walk Metropolis-Hastings (RW-MH) [ 23, 24] explore the posterior distribution too slowly to be useful in a high-dimensional setting.
While the transition kernels we employ in this paper successfully explore the posterior distribution for the three examples, we can envision datasets for which our algorithm would have difficulties producing accurate estimates of the posterior distribution.
However, exploring the posterior requires a high number of numerical simulations, which can make the problem impracticable within a given computational budget.
MCMC is a complex computational technique that requires some sophistication on the part of its users to ensure that it both converges and explores the posterior distribution properly.
A computationally feasible algorithm that explores the posterior distribution of the threshold models is designed using Markov chain Monte Carlo stochastic search methods.
Model parameter uncertainty is assessed by exploring the posterior probability distribution with the Metropolis algorithm, a Markov chain Monte Carlo sampling method.
In this paper, we study a fast approximate inference method based on expectation propagation for exploring the posterior probability distribution arising from the Bayesian formulation of nonlinear inverse problems.
