" Since the publication of the first edition, Bayesian statistics is, arguably, still not the norm in
the formal quantitative methods training of social scientists. Typically, the only introduction …

As a main task of inverse problem, model updating has received more and more attention in
the area of inspection, sensing, and monitoring technologies during the recent decades …

Abstract Models with a large number of latent variables are often used to utilize the
information in big or complex data, but can be difficult to estimate. Variational inference …

Estimation and prediction in high dimensional multivariate factor stochastic volatility models
is an important and active research area, because such models allow a parsimonious …

Stochastic variational inference algorithms are derived for fitting various heteroskedastic
time series models. We examine Gaussian, t, and skewed t response GARCH models and fit …

Parameter inference for linear and non-Gaussian state space models is challenging
because the likelihood function contains an intractable integral over the latent state …

Abstract Models with random effects, such as generalised linear mixed models (GLMMs),
are often used for analysing clustered data. Parameter inference with these models is …

Estimation and prediction in high dimensional multivariate factor stochastic volatility models
is an important and active research area because such models allow a parsimonious …

Time series datasets are often composed of a variety of sequences from the same domain,
but from different entities, such as individuals, products, or organizations. We are interested …

Time series datasets are usually composed of a variety of sequences from the same domain,
but from different entities, such as individuals, products, or organizations. We are interested …