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2 results

  • 13 - Variable Coefficients Models

  • Cheng Hsiao, University of Southern California
  • Book:
    Analysis of Panel Data
    Published online:
    19 May 2022
    Print publication:
    07 July 2022, pp 389-439

  • Summary

    The motivation for generalizing unobserved heterogeneity of varying parameter models is discussed. Various fixed or random varying parameters across cross-sectional units and over time models together with their respective inference procedures are introduced from both the sampling approach and the Bayesian approach. Issues of correlations between parameter variation and regressors are also discussed.

  • A sampling-based speaker clustering using utterance-oriented Dirichlet process mixture model and its evaluation on large-scale data

  • Naohiro Tawara, Tetsuji Ogawa, Shinji Watanabe, Atsushi Nakamura, Tetsunori Kobayashi
  • Journal:
    APSIPA Transactions on Signal and Information Processing / Volume 4 / 2015
    Published online by Cambridge University Press:
    28 October 2015, e16
    Print publication:
    2015
    • Article
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      • Open access
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  • An infinite mixture model is applied to model-based speaker clustering with sampling-based optimization to make it possible to estimate the number of speakers. For this purpose, a framework of non-parametric Bayesian modeling is implemented with the Markov chain Monte Carlo and incorporated in the utterance-oriented speaker model. The proposed model is called the utterance-oriented Dirichlet process mixture model (UO-DPMM). The present paper demonstrates that UO-DPMM is successfully applied on large-scale data and outperforms the conventional hierarchical agglomerative clustering, especially for large amounts of utterances.