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R Akella - One of the best experts on this subject based on the ideXlab platform.

  • a new probabilistic retrieval model based on the dirichlet compound Multinomial Distribution
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2008
    Co-Authors: Zuobing Xu, R Akella
    Abstract:

    The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to effective performance of the probabilistic models is the need to estimate a relevance model. The Dirichlet compound Multinomial (DCM) Distribution, which relies on hierarchical Bayesian modeling techniques, or the Polya Urn scheme, is a more appropriate generative model than the traditional Multinomial Distribution for text documents. We explore a new probabilistic model based on the DCM Distribution, which enables efficient retrieval and accurate ranking. Because the DCM Distribution captures the dependency of repetitive word occurrences, the new probabilistic model is able to model the concavity of the score function more effectively. To avoid the empirical tuning of retrieval parameters, we design several parameter estimation algorithms to automatically set model parameters. Additionally, we propose a pseudo-relevance feedback algorithm based on the latent mixture modeling of the Dirichlet compound Multinomial Distribution to further improve retrieval accuracy. Finally, our experiments show that both the baseline probabilistic retrieval algorithm based on the DCM Distribution and the corresponding pseudo-relevance feedback algorithm outperform the existing language modeling systems on several TREC retrieval tasks.

  • SIGIR - A new probabilistic retrieval model based on the dirichlet compound Multinomial Distribution
    Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '08, 2008
    Co-Authors: R Akella
    Abstract:

    The classical probabilistic models attempt to capture the Ad hoc information retrieval problem within a rigorous probabilistic framework. It has long been recognized that the primary obstacle to effective performance of the probabilistic models is the need to estimate a relevance model. The Dirichlet compound Multinomial (DCM) Distribution, which relies on hierarchical Bayesian modeling techniques, or the Polya Urn scheme, is a more appropriate generative model than the traditional Multinomial Distribution for text documents. We explore a new probabilistic model based on the DCM Distribution, which enables efficient retrieval and accurate ranking. Because the DCM Distribution captures the dependency of repetitive word occurrences, the new probabilistic model is able to model the concavity of the score function more effectively. To avoid the empirical tuning of retrieval parameters, we design several parameter estimation algorithms to automatically set model parameters. Additionally, we propose a pseudo-relevance feedback algorithm based on the latent mixture modeling of the Dirichlet compound Multinomial Distribution to further improve retrieval accuracy. Finally, our experiments show that both the baseline probabilistic retrieval algorithm based on the DCM Distribution and the corresponding pseudo-relevance feedback algorithm outperform the existing language modeling systems on several TREC retrieval tasks.

Masahiko Sagae - One of the best experts on this subject based on the ideXlab platform.

  • an exact cholesky decomposition and the generalized inverse of the variance covariance matrix of the Multinomial Distribution with applications
    Journal of the royal statistical society series b-methodological, 1992
    Co-Authors: Kunio Tanabe, Masahiko Sagae
    Abstract:

    A symbolic formula is given for the square-root-free Cholesky decomposition of the variance-covariance matrix of the Multinomial Distribution. The evaluation of the symbolic Cholesky factors requires much fewer arithmetic operations than does the general Cholesky algorithm. Since the symbolic formula is not affected by an ill-conditioned matrix, it is particularly useful when the elements of a probability vector are of quite different orders of magnitude. A simpler formula is obtained for Pederson's procedure of sampling from a Multinomial population. An explicit formula of the Moore-Penrose inverse of the variance-covariance matrix is given as well as a symmetric representation of a multinormal density approximation to the Multinomial Distribution. These formulae facilitate symmetric manipulation of the matrix and are useful in statistical modelling and computation involving the logistic density transformation of the Multinomial Distribution and in computer simulations of dynamic models in population genetics. Each element of the Cholesky factors is given interesting probabilistic interpretations.

  • Symbolic Cholesky decomposition of the variance-covariance matrix of the negative Multinomial Distribution
    Statistics & Probability Letters, 1992
    Co-Authors: Masahiko Sagae, Kunio Tanabe
    Abstract:

    This note shows a symbolic formula for the square-root-free Cholesky decomposition of the variance--covariance matrix of the negative Multinomial Distribution. A similar decomposition was given for the Multinomial case by Tanabe and Sagae (1984). The evaluation of the symbolic Cholesky factors requires much less arithmetic operations than those with the general Cholesky algorithm. It is applied to obtain a recursive algorithm for generating multivariate normal random numbers which simulate samples from a negative Multinomial population, which is similar to Pederson's procedure for sampling from Multinomial populations. An explicit formula of a multivariate normal density approximation to negative Multinomial Distribution is also given.

Kunio Tanabe - One of the best experts on this subject based on the ideXlab platform.

  • an exact cholesky decomposition and the generalized inverse of the variance covariance matrix of the Multinomial Distribution with applications
    Journal of the royal statistical society series b-methodological, 1992
    Co-Authors: Kunio Tanabe, Masahiko Sagae
    Abstract:

    A symbolic formula is given for the square-root-free Cholesky decomposition of the variance-covariance matrix of the Multinomial Distribution. The evaluation of the symbolic Cholesky factors requires much fewer arithmetic operations than does the general Cholesky algorithm. Since the symbolic formula is not affected by an ill-conditioned matrix, it is particularly useful when the elements of a probability vector are of quite different orders of magnitude. A simpler formula is obtained for Pederson's procedure of sampling from a Multinomial population. An explicit formula of the Moore-Penrose inverse of the variance-covariance matrix is given as well as a symmetric representation of a multinormal density approximation to the Multinomial Distribution. These formulae facilitate symmetric manipulation of the matrix and are useful in statistical modelling and computation involving the logistic density transformation of the Multinomial Distribution and in computer simulations of dynamic models in population genetics. Each element of the Cholesky factors is given interesting probabilistic interpretations.

  • Symbolic Cholesky decomposition of the variance-covariance matrix of the negative Multinomial Distribution
    Statistics & Probability Letters, 1992
    Co-Authors: Masahiko Sagae, Kunio Tanabe
    Abstract:

    This note shows a symbolic formula for the square-root-free Cholesky decomposition of the variance--covariance matrix of the negative Multinomial Distribution. A similar decomposition was given for the Multinomial case by Tanabe and Sagae (1984). The evaluation of the symbolic Cholesky factors requires much less arithmetic operations than those with the general Cholesky algorithm. It is applied to obtain a recursive algorithm for generating multivariate normal random numbers which simulate samples from a negative Multinomial population, which is similar to Pederson's procedure for sampling from Multinomial populations. An explicit formula of a multivariate normal density approximation to negative Multinomial Distribution is also given.

M N Vrahatis - One of the best experts on this subject based on the ideXlab platform.

  • tracking particle swarm optimizers an adaptive approach through Multinomial Distribution tracking with exponential forgetting
    Congress on Evolutionary Computation, 2012
    Co-Authors: Michael G Epitropakis, D K Tasoulis, Nicos G Pavlidis, Vassilis P Plagianakos, M N Vrahatis
    Abstract:

    An active research direction in Particle Swarm Optimization (PSO) is the integration of PSO variants in adaptive, or self-adaptive schemes, in an attempt to aggregate their characteristics and their search dynamics. In this work we borrow ideas from adaptive filter theory to develop an “online” algorithm adaptation framework. The proposed framework is based on tracking the parameters of a Multinomial Distribution to capture changes in the evolutionary process. As such, we design a Multinomial Distribution tracker to capture the successful evolution movements of three PSO variants. Extensive experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising. On the majority of tested cases, the proposed framework achieves substantial performance gain, while it seems to identify accurately the most appropriate algorithm for the problem at hand.

  • tracking differential evolution algorithms an adaptive approach through Multinomial Distribution tracking with exponential forgetting
    Hellenic Conference on Artificial Intelligence, 2012
    Co-Authors: Michael G Epitropakis, D K Tasoulis, Nicos G Pavlidis, Vassilis P Plagianakos, M N Vrahatis
    Abstract:

    Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an "online" algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a Multinomial Distribution to reflect changes in the evolutionary process. As such, we design a Multinomial Distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.

  • SETN - Tracking differential evolution algorithms: an adaptive approach through Multinomial Distribution tracking with exponential forgetting
    Lecture Notes in Computer Science, 2012
    Co-Authors: Michael G Epitropakis, D K Tasoulis, Nicos G Pavlidis, Vassilis P Plagianakos, M N Vrahatis
    Abstract:

    Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an "online" algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a Multinomial Distribution to reflect changes in the evolutionary process. As such, we design a Multinomial Distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.

Ian G Taylor - One of the best experts on this subject based on the ideXlab platform.

  • model based estimates of effective sample size in stock assessment models using the dirichlet Multinomial Distribution
    Fisheries Research, 2017
    Co-Authors: James T Thorson, Kelli F Johnson, Richard D Methot, Ian G Taylor
    Abstract:

    Abstract Theoretical considerations and applied examples suggest that stock assessments are highly sensitive to the weighting of different data sources whenever data sources conflict regarding parameter estimates. Previous iterative reweighting approaches to weighting compositional data are generally ad hoc, do not propagate uncertainty about data-weighting when calculating uncertainty intervals, and often are not re-adjusted when conducting sensitivity or retrospective analyses. We therefore incorporate the Dirichlet-Multinomial Distribution into Stock Synthesis, and propose it as a model-based method for estimating effective sample size. This Distribution incorporates one additional parameter per fleet (with the option of mirroring its value among fleets), and we show that this parameter governs the ratio of nominal (“input”) and effective (“output”) sample size. We demonstrate this approach using data for Pacific hake, where the Dirichlet-Multinomial Distribution and an iterative reweighting approach previously developed by McAllister and Ianelli (1997) give similar results. We also use simulation testing to explore the estimation properties of this new estimator, and show that it provides approximately unbiased estimates of variance inflation when compositional samples capture clusters of individuals with similar ages/lengths. We conclude by recommending further research to develop computationally efficient estimators of effective sample size that are based on alternative, a priori consideration of sampling theory and population biology.