Moment-matching approximations for stochastic sums in non-Gaussian Ornstein–Uhlenbeck models

Exponential Models: Approximations for Probabilities

Welch & Peers (1963) used a root-information prior to obtain posterior probabilities for a scalar parameter exponential model and showed that these Bayes probabilities had the confidence property to second order asymptotically. An important undercurrent of this indicates that the constant information reparameterization provides location model structure, for which the confidence property ...

Novel moment closure approximations in stochastic epidemics.

Moment closure approximations are used to provide analytic approximations to non-linear stochastic population models. They often provide insights into model behaviour and help validate simulation results. However, existing closure schemes typically fail in situations where the population distribution is highly skewed or extinctions occur. In this study we address these problems by introducing n...

This Issue : Non - Gaussian Stochastic Models

Validity conditions for moment closure approximations in stochastic chemical kinetics.

Approximations based on moment-closure (MA) are commonly used to obtain estimates of the mean molecule numbers and of the variance of fluctuations in the number of molecules of chemical systems. The advantage of this approach is that it can be far less computationally expensive than exact stochastic simulations of the chemical master equation. Here, we numerically study the conditions under whi...

Variant functional approximations for latent Gaussian models

The integrated nested Laplace approximation (INLA) [Rue et al., 2009] is now a well-known functional approximation algorithm for implementing Bayesian inference in latent Gaussian models but has some limitations: it is unable to handle a high dimensional model parameter θ, and makes a poor approximation when the posterior is multi-modal and the likelihood is highly nonGaussian. Two types of alg...