We describe the implementation of a hierarchical constrained Bayesian Optimization algorithm and it’s application to joint optimization of features, acoustic model structure and decoding parameters for deep neural network (DNN)-based large vocabulary continuous speech recognition (LVCSR) systems. Within our hierarchical optimization method we perform constrained Bayesian optimization jointly of...

Genetic epidemiological methodologies, such as linkage analysis, often require accurate estimates of allele frequencies. When studies involve multiple sub-populations with different evolutionary histories, accurate estimates can be difficult to obtain because the number of subjects per sub-population tends to be limited. Given allele counts for a collection of loci and sub-populations, we propo...

BACKGROUND Bayesian hierarchical models have been proposed to combine evidence from different types of study designs. However, when combining evidence from randomised and non-randomised controlled studies, imbalances in patient characteristics between study arms may bias the results. The objective of this study was to assess the performance of a proposed Bayesian approach to adjust for imbalanc...

The Sloan Digital Sky Survey (SDSS) is an extremely large astronomical survey conducted with the intention of mapping more than a quarter of the sky (http://www.sdss.org/). Among the data it is generating are spectroscopic and photometric measurements, both allowing estimation of the redshift of galaxies. The former is precise but expensive to gather, the latter is far cheaper but corresponding...

Many streams of real-world data, such as conversations or body movements, consist of relatively coherent segments, each characterized by particular topics or controllers. Making sense of these data requires simultaneously segmenting the sequences and inferring the structure of the segments. We present a hierarchical Bayesian model that can be used to break a sequence of utterances or movements ...

There is a long history in the social sciences of building models to describe processes related to arms races. These include both theoretical models, such as the actionreaction Richardson model, and empirical models. In addition, there is a substantial literature on estimating parameters associated with these models given observations of military expenditures, or various proxies. Such methods s...

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the...

Hierarchical models are rather common in uncertainty theory. They arise when there is a ‘correct’ or ‘ideal’ (so-called first-order) uncertainty model about a phenomenon of interest, but the modeler is uncertain about what it is. The modeler’s uncertainty is then called second-order uncertainty. For most of the hierarchical models in the literature, both the firstand the second-order models are...

In this technical report, we review the Bayesian melding model, proposed by Fuentes and Raftery (2005). We give technical details about using Gibbs sampling algorithm to fit the model and critically assess the model through various simulation studies and applications of combing ozone measurements with simulated ozone levels from deterministic model AQM. keywords: Bayesian melding, Bayesian hier...