In addition to being accurate, it is important that diagnostic systems for use in automobiles also have low development and hardware costs. Model-based methods have shown promise at reducing hardware costs since they use analytical redundancy to reduce physical redundancy. In addition to requiring no extra sensors, the diagnostic system presented in this paper also allows for high accuracy and ...

We begin by recalling the core Bayesian hierarchical modeling result of Lindley and Smith (1972), henceforth abbreviated L&S. For n × p1-dimensional response vector z, p1-vector of parameters θ, known n× p1 design matrix A1, and known n× n covariance matrix C1, let the likelihood be z ∼ N(A1θ, C1). Then for second-level p2-vector of parameters μ, known design and covariance matrices A2 and C2, ...

Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a “causal design,” i.e., without anticipating indirect influences appropriately in time, we ...

conclusions it was noted that a relatively high prevalence of growth failure was observed in the study sample. for minimizing the impact of significant risk factors on growth failure, the early detection of growth failure and its risk indicators is of great importance. in addition, when the focus of the analysis is on the different nested sources of variability and the data has a hierarchical s...

This paper uses Bayesian modeling techniques to analyze a data set extracted from the British General Household survey. The models used are Bayesian networks, which provide a compact and easy to interpret knowledge representation formalism. An issue considered is the need for automated Bayesian modeling.

Bayesian methods have proven to be powerful tools for computed tomographic reconstruction in realistic physical problems. However, Bayesian methods require that a number of modeling and computational problems be addressed. This paper summarizes a coherent system of statistical modeling and optimization techniques designed to facilitate efficient, unsupervised Bayesian emission and transmission ...

OF THE DISSERTATION Networks of Mixture Blocks for Non Parametric Bayesian Models with Applications By Ian Porteous Doctor of Philosophy in Information and Computer Science University of California, Irvine, 2010 Professor Max Welling, Chair This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficien...

OF THE DISSERTATION Mixture Block Methods for Non Parametric Bayesian Models with Applications By Ian Porteous Doctor of Philosophy in Computer Science University of California, Irvine, 2010 Professor Max Welling, Chair This study brings together Bayesian networks, topic models, hierarchical Bayes modeling and nonparametric Bayesian methods to build a framework for efficiently designing and imp...