Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular ...

In this paper we propose a method to calculate the posterior probability of a nondecomposable graphical Gaussian model. Our proposal is based on a new device to sample from Wishart distributions, ...

Graphical models: Bayesian networks, Conditional independence, Markov random fields. Mixture models and Clustering: Clustering, Mixtures, The EM algorithm. Sampling methods: Basic sampling algorithms, ...

We present a Bayesian backtesting framework for CCR models that addresses a number of limitations of the common backtesting approach based on null hypothesis significance testing. The approach is ...

Chordal Markov networks are a central class of undirected graphical models. Being equivalent to so-called decomposable models, they are essentially a special case of Bayesian networks. This thesis ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

We consider Bayesian inference and propose using the multiplicative (or Chung–Lu random graph) model as a prior on the graphical space. In the multiplicative model, each edge is chosen independently ...

The course will introduce the basic principles and algorithms used in Bayesian machine learning. This will include the Bayesian approach to regression and classification tasks, introduction to the ...