Latent Gaussian models are flexible models that are applied in several statistical applications. When posterior marginals or full conditional distributions in hierarchical Bayesian inference from these models are not available in closed form, Markov chain Monte Carlo methods are implemented. The component dependence of the latent field usually causes increase in computational time and divergenc...

Hierarchical Control Flow Graph Models deene a modeling paradigm for discrete event simulation modeling based upon hierarchical extensions to Control Flow Graph Models. Conceptually, models consist of a set of encapsulated, concurrently operating model components that interact solely via message passing. The primary objectives of Hierarchical Control Flow Graph Models are: (1) to facilitate mod...

Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area statistics. Traditional area-specific estimators may not provide adequate precision because sample sizes in small areas are seldom large enough. This makes it necessary to employ indirect estimators based on linking models. Basic area level and unit level models have been extensiv...

We formulate a model for probability distributions on image spaces. We show that any distribution of images can be factored exactly into conditional distributions of feature vectors at one resolution (pyramid level) conditioned on the image information at lower resolutions. We would like to factor this over positions in the pyramid levels to make it tractable, but such factoring may miss long-r...

In many models, variances are assumed to be constant although this assumption is known to be unrealistic. Joint modelling of means and variances can lead to infinite probability densities which makes it a difficult problem for many learning algorithms. We show that a Bayesian variational technique which is sensitive to probability mass instead of density is able to jointly model both variances ...

This paper describes a general model that subsumes many parametric models for continuous data. The model comprises hidden layers of state-space or dynamic causal models, arranged so that the output of one provides input to another. The ensuing hierarchy furnishes a model for many types of data, of arbitrary complexity. Special cases range from the general linear model for static data to general...

LANGUAGE MODELS FOR HIERARCHICAL SUMMARIZATION

Empirical hardness models predict a solver’s runtime for a given instance of an NP-hard problem based on efficiently computable features. Previous research in the SAT domain has shown that better prediction accuracy and simpler models can be obtained when models are trained separately on satisfiable and unsatisfiable instances. We extend this work by training separate hardness models for each c...

Hierarchical latent class (HLC) models generalize latent class models. As models for cluster analysis, they suit more applications than the latter because they relax the often untrue conditional independence assumption. They also facilitate the discovery of latent causal structures and the induction of probabilistic models that capture complex dependencies and yet have low inferential complexit...

Bayesian data analysis involves describing data by meaningful mathematical models, and allocating credibility to parameter values that are consistent with the data and with prior knowledge. The Bayesian approach is ideally suited for constructing hierarchical models, which are useful for data structures with multiple levels, such as data from individuals who are members of groups which in turn ...