Search Constraints

Start Over
Filtering by: Creator Tadepalli, Prasad Remove constraint Creator: Tadepalli, Prasad Subject Bayesian statistical decision theory Remove constraint Subject: Bayesian statistical decision theory

Search Results

  • Bayesian methods for knowledge transfer and policy search in reinforcement learning

    Creator:
    Wilson, Aaron
    Abstract:
    How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
    Resource Type:
    Dissertation
    Full Text:
    from a new researcher highlighted the value of a curious spirit. Prasad Tadepalli has acted as a

  • Optimal inference with local expressions

    Creator:
    Borujerdi, Mohammad
    Abstract:
    Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many e ciency-driven tech- niques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization point of view on the problem. The contribution of this thesis is to extend...
    Resource Type:
    Dissertation
    Full Text:
    Member (Timothy Budd) Committee Member (Prasad Tadepalli) Graduate School Representative (Jonathan King

  • Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling

    Creator:
    Wang, Haiou
    Abstract:
    Logic Sampling, Likelihood Weighting and AIS-BN are three variants of stochastic sampling, one class of approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the relationship among them. The results from a set of empirical experiments comparing Logic Sampling, Likelihood Weighting and AIS-BN are presented. We...
    Resource Type:
    Masters Thesis
    Full Text:
    . Tadepalli Prasad and the anonymous referees. TABLE OF CONTENTS Page Chapter 1: Introduction 1 Chapter

  • Constraining Bayesian network learning with qualitative models

    Creator:
    Altendorf, Eric E.
    Abstract:
    Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
    Resource Type:
    Masters Thesis
    Full Text:
    , Sriraam Natarajan, Angelo Restificar, and Prasad Tadepalli. Angelo Restificar spent a significant amount

  • Representations and algorithms for efficient inference in Bayesian networks

    Creator:
    Takikawa, Masami
    Abstract:
    Bayesian networks are used for building intelligent agents that act under uncertainty. They are a compact representation of agents' probabilistic knowledge. A Bayesian network can be viewed as representing a factorization of a full joint probability distribution into the multiplication of a set of conditional probability distributions. Independence of causal...
    Resource Type:
    Dissertation
    Full Text:
    . Bella Bose, Dr. Timothy A. Budd, Dr. Prasad Tadepalli, and Dr. Terry M. Wood for their helpful

  • Analysis of Bayesian anytime inference algorithms

    Creator:
    Burgess, Scott Alan
    Abstract:
    This dissertation explores and analyzes the performance of several Bayesian anytime inference algorithms for dynamic influence diagrams. These algorithms are compared on the On-Line Maintenance Agent testbed, a software artifact permitting comparison of dynamic reasoning algorithms used by an agent on a variety of simulated maintenance and monitoring tasks. Analysis...
    Resource Type:
    Dissertation
    Full Text:
    Prasad Tadepalli for their input, accessibility, and help in innumerable ways; Dr. Margaret Niess, my