Bayesian Inference and Maximum Entropy Methods in Science and Engineering: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Rainer Fischer, Roland Preuss, Udo von Toussaint
American Inst. of Physics, Nov 19, 2004 - Mathematics - 605 pages
All papers were peer reviewed. Bayesian Inference and Maximum Entropy Methods in Science and Engineering provide a framework for analyzing ill-conditioned data. Maximum Entropy is a theoretical method to draw conclusions when little information is available. Bayesian probability theory provides a formalism for scientific reasoning by analyzing noisy or imcomplete data using prior knowledge.
What people are saying - Write a review
We haven't found any reviews in the usual places.
Bayesian Wavelet Domain Segmentation
Multigrid Priors for fMRI Time Series Analysis
Model Fitting and Model Evidence for Multiscale Image
47 other sections not shown
2004 American Institute algorithm applied approximation assume Bayes Bayesian Inference Bayesian probability bitnet calculated Chain Monte Carlo cluster coefficients components computed consider constant constraints corresponding Dasher data analysis decay defined denote diffusion equation estimate example exponential factor FIGURE Fischer frequency Gaussian given graph hyperparameters independent Independent Component Analysis Inference and Maximum Institute of Physics integral iterations likelihood function linear marginal Markov chain Markov Chain Monte matrix MaxEnt Maximum Entropy Methods MCMC measure Methods in Science minimization mixture model model selection neural network noise normal nuisance parameters observations obtained optimal pixel plasma policyholder posterior distribution posterior probability POVM predictive Preuss prior distribution prior probability probabilistic probability distribution problem random relevant sample scale Science and Engineering segmentation sensor signal simulation solution source separation space spectral statistical structure theorem theory Toussaint uncertainty values variables variance vector wavelet