• Title/Summary/Keyword: Gamma-Makov chain

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Bayesian Analysis in Generalized Log-Gamma Censored Regression Model

  • Younshik chung;Yoomi Kang
    • Communications for Statistical Applications and Methods
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    • v.5 no.3
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    • pp.733-742
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    • 1998
  • For industrial and medical lifetime data, the generalized log-gamma regression model is considered. Then the Bayesian analysis for the generalized log-gamma regression with censored data are explained and following the data augmentation (Tanner and Wang; 1987), the censored data is replaced by simulated data. To overcome the complicated Bayesian computation, Makov Chain Monte Carlo (MCMC) method is employed. Then some modified algorithms are proposed to implement MCMC. Finally, one example is presented.

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Speech Enhancement Using Nonnegative Matrix Factorization with Temporal Continuity (시간 연속성을 갖는 비음수 행렬 분해를 이용한 음질 개선)

  • Nam, Seung-Hyon
    • The Journal of the Acoustical Society of Korea
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    • v.34 no.3
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    • pp.240-246
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    • 2015
  • In this paper, speech enhancement using nonnegative matrix factorization with temporal continuity has been addressed. Speech and noise signals are modeled as Possion distributions, and basis vectors and gain vectors of NMF are modeled as Gamma distributions. Temporal continuity of the gain vector is known to be critical to the quality of enhanced speech signals. In this paper, temporal continiuty is implemented by adopting Gamma-Markov chain priors for noise gain vectors during the separation phase. Simulation results show that the Gamma-Markov chain models temporal continuity of noise signals and track changes in noise effectively.