## Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 22nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Moscow, Idaho, 3-7 August 2002The papers for these proceedings were peer reviewed. Bayesian inference and maximum entropy methods provide a framework for analyzing very complicated data sets. The papers in this volume provide applications of these methods to problems such as medical imaging, weather prediction, intrusion detection, and modeling planetary nebulae. Other papers address foundational questions that underlie these methods. Topics include: estimation and inference; applications in physics; signal separation and classification; inductive logic theory; prior specification; and tutorials. |

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### Contents

Frequency Estimation Multiple Stationary Nonsinusoidal Resonances | 3 |

A Bayesian Approach to Estimating Coupling between Neural | 23 |

Bayesian Estimation of Fish Disease Prevalence from Pooled Samples | 39 |

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algorithm amplitude analysis applied approximation assignment assume attenuation axioms Bayes Bayesian approach Bayesian Inference Boolean lattice bound boundary conditions clutter component compute consider constraints convergence convex coordinates data set defined denote density function derived discrimination function elements entropy production equation estimate example Figure fractiles frequency fusion Gaussian geometry given hyperbolic images Independent Component Analysis Inference and Maximum inputs inquiry integral iterations latency light curve likelihood function linear logical Markov chain matrix MaxEnt Maximum Entropy Methods maximum likelihood mcERP mean measure Methods in Science minimum mixture model Mohammad-Djafari nebula Neural noise notation number of resonances object observer obtained optimal parameters periodogram pixels posterior distribution posterior probability principle prior distributions prior probability probability distribution problem question radar relation sample Science and Engineering SigHat signal simulations solution source separation space Statistics Support Vector Machine Theorem theory transformation utility values variable variance wavelet coefficients waveshapes zero