Post-Inference Methods for Scalable Probabilistic Modeling by Willie Neiswanger This thesis focuses on post-inference methods, which are procedures that can be applied after the completion of standard inference algorithms to allow for increased efficiency, accuracy, or parallelism when learning probabilistic models of big data sets. These methods also aim to allow for efficient computation give...

We propose that probabilistic inference is supported by a mental toolbox that includes sampling and symmetry-based reasoning in addition to several other methods. To flesh out this claim we consider a spatial reasoning task and describe a number of different methods for solving the task. Several recent process-level accounts of probabilistic inference have focused on sampling, but we present an...

A new Bayesian framework for 3–D object classification and localization is introduced. Objects are represented as probability density functions, and observed features are treated as random variables. These probability density functions turn out a non geometric nature of models and characterize the statistical behavior of local object features like points or lines. The parameterization of model ...

We consider nonlinear stochastic programming problems with probabilistic constraints. The concept of a p-efficient point of a probability distribution is used to derive equivalent problem formulations, and necessary and sufficient optimality conditions. We analyze the dual functional and its subdifferential. Two numerical methods are developed based on approximations of the p-efficient frontier...

A catalogue record for this book is available from the British Library Library of Congress Cataloguing in Publication data Bedford T. Mathematical tools for probabilistic risk analysis / Tim Bedford and Roger M. Cooke. p. cm. Includes bibliographical references and index.

This report contains the description of the main methods, implemented in ASTRA 3.0, to analyse coherent andnon-coherent fault trees. ASTRA 3.0 is fully based on the Binary Decision Diagrams (BDD) approach. In thecase of non-coherent fault trees ASTRA 3.0 dynamically assigns to each node of the graph a label that identifiesthe type of the associated variable in order to drive the...

This paper summarizes our progress in using probabilistic methods to enable both singledomain and multiple-domain considerations in lifecycle design. As an example of a single-domain application, a probability-based reliability model that describes the effect of remanufacture on the reliability of parts and systems is outlined and experimentally verified. This reliability model is applied to fa...

I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This can provide intuitive guidelines for choosing a 'good' SVM kernel. It can also assign (by evidence maximization) optimal values to parameters such as the noise level C which cannot be determined unambiguously from properties of t...

There are many known applications of the Probabilistic Method in Extremal Finite Set Theory. In this paper we describe several examples, demonstrating some of the techniques used and illustrating some of the typical results obtained. This is partly a survey paper, but it also contains various new results. ∗Research supported in part by a United States Israel BSF Grant and by a Bergmann Memorial...

This special volume of the ESAIM Journal, Mathematical Modelling and Numerical Analysis, contains a collection of articles on probabilistic interpretations of some classes of nonlinear integrodifferential equations. The selected contributions deal with a wide range of topics in applied probability theory and stochastic analysis, with applications in a variety of scientific disciplines, includin...