Joint species distribution modelling (JSDM) is a fast-developing field and promises to
revolutionise how data on ecological communities are analysed and interpreted. Written for …

We propose an efficient way to sample from a class of structured multivariate Gaussian
distributions. The proposed algorithm only requires matrix multiplications and linear system …

Symmetric binary matrices representing relations are collected in many areas. Our focus is
on dynamically evolving binary relational matrices, with interest being on inference on the …

Capturing predictor-dependent correlations amongst the elements of a multivariate
response vector is fundamental to numerous applied domains, including neuroscience …

Our focus is on realistically modeling and forecasting dynamic networks of face-to-face
contacts among individuals. Important aspects of such data that lead to problems with …

We propose a targeted and robust modeling of dependence in multivariate time series via
dynamic networks, with time-varying predictors included to improve interpretation and …

Gaussian processes are a fundamental statistical tool used in a wide range of applications.
In the spatiotemporal setting, several families of covariance functions exist to accommodate …

We present a comprehensive simulation study to assess and compare the performance of
popular machine learning algorithms for time series prediction tasks. Specifically, we …

Many Bayesian nonparametric approaches to multivariate time series rely on Whittle's
Likelihood, involving the second order structure of a stationary time series by means of its …

Bayesian Functional Data Modeling for Heterogeneous Volatility Page 1 Bayesian Analysis (2017)
12, Number 2, pp. 335–350 Bayesian Functional Data Modeling for Heterogeneous Volatility …