Titlebook: Numerical Bayesian Methods Applied to Signal Processing; Joseph J. K. Ó Ruanaidh,William J. Fitzgerald Book 1996 Springer-Verlag New York,

平淡而无味

Retrospective Changepoint Detection,ical applications abound in diverse areas such as medicine (e.g. monitoring of drug levels in hospital patients), the detection of kickback in oil well pressure data [140] and edge detection in images [123]. In this chapter, optimal Bayesian techniques are developed for changepoint identification in

BURSA

Restoration of Missing Samples in Digital Audio Signals,digital audio signals. The section of audio signal in question is modelled as a stationary autoregressive process, and missing samples are imputed using the Gibbs sampler. The corresponding ML and EM algorithm solutions to the problem are developed and discussed, and the results are compared for bot

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Integration in Bayesian Data Analysis,begins with an example for which the Bayesian evidence (or rather the integrated likelihood) and marginal densities may be computed in closed form. These numerical techniques are then applied to a difficult problem in data analysis; namely, inferring the number of decaying exponentials and the value

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Conclusion,ts in this work. First, we considered optimisation for the location of the posterior mode. Second, we investigated different strategies for integrating the posterior density. Third, we attempted to simulate random samples from the posterior density.

expansive

Statistics and Computinghttp://image.papertrans.cn/n/image/668964.jpg

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climax

Introduction,s been in existence for decades and has extensive applications to the fields of speech and data communications, biomedical engineering, acoustics, sonar, radar, seismology, oil exploration, instrumentation and audio signal processing to name but a few [87].

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Entirety

Conclusion,ts in this work. First, we considered optimisation for the location of the posterior mode. Second, we investigated different strategies for integrating the posterior density. Third, we attempted to simulate random samples from the posterior density.

诱使

Probabilistic Inference in Signal Processing,In this chapter, the fundamental concepts and techniques underlying Bayesian inference are reviewed. We begin with a definition of the key problem of data analysis, which is to interpret data in the presence of noise. We advocate a Bayesian approach to this problem.