Titlebook: Maximum Entropy and Bayesian Methods; Santa Fe, New Mexico Kenneth M. Hanson,Richard N. Silver Conference proceedings 1996 Kluwer Academic

incontestable

書目名稱Maximum Entropy and Bayesian Methods影響因子(影響力)




書目名稱Maximum Entropy and Bayesian Methods影響因子(影響力)學(xué)科排名




書目名稱Maximum Entropy and Bayesian Methods網(wǎng)絡(luò)公開度




書目名稱Maximum Entropy and Bayesian Methods網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Maximum Entropy and Bayesian Methods被引頻次




書目名稱Maximum Entropy and Bayesian Methods被引頻次學(xué)科排名




書目名稱Maximum Entropy and Bayesian Methods年度引用




書目名稱Maximum Entropy and Bayesian Methods年度引用學(xué)科排名




書目名稱Maximum Entropy and Bayesian Methods讀者反饋




書目名稱Maximum Entropy and Bayesian Methods讀者反饋學(xué)科排名

Torrid

孤獨(dú)無助

The De Finetti Transform, theorem to define the “de Finetti transform”, which permits us, under many frequently occurring conditions (such as natural conjugacy), to find the unique sampling density (conditional on some indexing parameters), and the unique associated prior distribution for those parameters.

懶鬼才會(huì)衰弱

Mechanical Models as Priors in Bayesian Tomographic Reconstruction,ion-Maximization deterministic annealing algorithms to alleviate this problem. Our simulation studies qualitatively demonstrate the improvements over the weak membrane and maximum likelihood reconstructions.

CRATE

prosperity

HILAR

The Bayes Inference Engine, a data-flow diagram that may be manipulated by the analyst through a graphical- programming environment. Maximum a posteriori solutions are achieved using a general, gradient-based optimization algorithm. The application incorporates a new technique of estimating and visualizing the uncertainties in specific aspects of the model.

Ganglion

Priapism

Bayesian Time Series: Models and Computations for the Analysis of Time Series in the Physical Scienbseries; nonlinear time series models based on mixtures of auto-regressions; problems with errors and uncertainties in the timing of observations; and the development of non-linear models based on stochastic deformations of time scales.

一起

Bayesian Estimation of the Von Mises Concentration Parameter,ge Length (MML) principle. Here, we examine a variety of Bayesian estimation techniques by examining the posterior distribution in both polar and Cartesian co-ordinates. We compare the MML estimator with these fellow Bayesian techniques, and a range of Classical estimators. We find that the Bayesian estimators outperform the Classical estimators.