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Bayesian Method

The Experts below are selected from a list of 39417 Experts worldwide ranked by ideXlab platform

Edward Herskovits – 1st expert on this subject based on the ideXlab platform

  • a Bayesian Method for constructing Bayesian belief networks from databases
    arXiv: Artificial Intelligence, 2013
    Co-Authors: Gregory F Cooper, Edward Herskovits

    Abstract:

    This paper presents a Bayesian Method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We relate the Methods in this paper to previous work, and we discuss open problems.

  • A Bayesian Method for the Induction of Probabilistic Networks from Data
    Machine Learning, 1992
    Co-Authors: Gregory F Cooper, Edward Herskovits

    Abstract:

    This paper presents a Bayesian Method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic Method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the Methods in this paper to previous work, and we discuss open problems.

Keiichi Tokuda – 2nd expert on this subject based on the ideXlab platform

  • Image recognition based on hidden Markov eigen-image models using variational Bayesian Method
    2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, 2013
    Co-Authors: Kei Sawada, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

    Abstract:

    An image recognition Method based on hidden Markov eigen-image models (HMEMs) using the variational Bayesian Method is proposed and experimentally evaluated. HMEMs have been proposed as a model with two advantageous properties: linear feature extraction based on statistical analysis and size-and-location-invariant image recognition. In many image recognition tasks, it is difficult to use sufficient training data, and complex models such as HMEMs suffer from the over-fitting problem. This study aims to accurately estimate HMEMs using the Bayesian criterion, which attains high generalization ability by using prior information and marginalization of model parameters. Face recognition experiments showed that the proposed Method improves recognition performance.

  • Face recognition based on separable lattice 2-D HMMS using variational Bayesian Method
    2012 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2012
    Co-Authors: Kei Sawada, Akira Tamamori, Kei Hashimoto, Yoshihiko Nankaku, Keiichi Tokuda

    Abstract:

    This paper proposes an image recognition technique based on separable lattice 2-D HMMs (SL2D-HMMs) using the variational Bayesian Method. SL2D-HMMs have been proposed to reduce the effect of geometric variations, e.g., size and location. The maximum likelihood criterion had previously been used in training SL2D-HMMs. However, in many image recognition tasks, it is difficult to use sufficient training data, and it suffers from the over-fitting problem. A higher generalization ability based on model marginalization is expected by applying the Bayesian criterion and useful prior information on model parameters can be utilized as prior distributions. Experiments on face recognition indicated that the proposed Method improved image recognition.

Thomas Rodet – 3rd expert on this subject based on the ideXlab platform

  • A subspace-based variational Bayesian Method
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuling Zheng, Aurélia Fraysse, Thomas Rodet

    Abstract:

    This paper is devoted to an improved variational Bayesian Method. Actually, variational Bayesian issue can be seen as a convex functional optimization problem. Our main contribution is the adaptation of subspace optimization Methods into the functional space involved in this problem. We highlight the efficiency of our Methodology on a linear inverse problem with a sparse prior. Comparisons with classical Bayesian Methods through a numerical example show the notable improved computation time.