Publisher Summary Mixture distributions comprise a finite or infinite number of components,
possibly of different distributional types, that can describe different features of data. The …

Bayesian forecasting is a natural product of a Bayesian approach to inference. The
Bayesian approach in general requires explicit formulation of a model, and conditioning on …

The fourth edition follows the general layout of the third edition but includes some
modernization of topics as well as the coverage of additional topics. The preface to the third …

Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

This paper compares alternative models of time‐varying volatility on the basis of the
accuracy of real‐time point and density forecasts of key macroeconomic time series for the …

A mixture innovation time-varying parameter VAR model is used to examine the impact of
structural oil price shocks on US stock market return. Time variation is evident in both the …

This paper investigates whether the monetary transmission mechanism has changed or
whether apparent changes are due to changes in the volatility of exogenous shocks. Also …

Inference for multiple-equation Markov-chain models raises a number of difficulties that are
unlikely to appear in smaller models. Our framework allows for many regimes in the …

Multibeam echosounders (MBES) are currently the best way to determine the bathymetry of
large regions of the seabed with high accuracy. They are becoming the standard instrument …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …