標題: Titlebook: Maximum Entropy and Bayesian Methods; Santa Fe, New Mexico Kenneth M. Hanson,Richard N. Silver Conference proceedings 1996 Kluwer Academic [打印本頁] 作者: incontestable 時間: 2025-3-21 19:08
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讀者反饋學科排名
作者: Torrid 時間: 2025-3-21 20:48 作者: 孤獨無助 時間: 2025-3-22 02:15
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.作者: 懶鬼才會衰弱 時間: 2025-3-22 08:00
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.作者: CRATE 時間: 2025-3-22 10:17 作者: prosperity 時間: 2025-3-22 16:07 作者: HILAR 時間: 2025-3-22 18:10
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.作者: Ganglion 時間: 2025-3-22 22:19 作者: Priapism 時間: 2025-3-23 02:41
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.作者: 一起 時間: 2025-3-23 06:30
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.作者: 原告 時間: 2025-3-23 11:24
Model Selection and Parameter Estimation for Exponential Signals,ials is the correct value. When the number of exponentials in the data are well determined, this reduces to the problem of determining the number of exponentials and then estimating the parameters given that number of exponentials. However, when the data fail to strongly support a single value for t作者: infinite 時間: 2025-3-23 16:45
Bayesian Multimodal Evidence Computation by Adapti Tempering MCMC,meters of interest and ?. The Gibbs sampler is then employed to simulate this joint distribution and an importance reweight- ing procedure is incorporated for adaptively updating the ratios of the partition functions. The thermodynamic integration approach is adopted to evaluate the partition functi作者: LINES 時間: 2025-3-23 20:34
Anand Ramaswami,G. Larry Bretthorst at the end of each chapter, and the material is organized for self study as well as classroom use. The material is the most recent and timely in capturing the state-of-the-art in the fast-moving field of optical WDM networking..978-1-4899-7883-7978-0-387-29188-8Series ISSN 1935-3839 Series E-ISSN 1935-3847 作者: Texture 時間: 2025-3-23 23:24
D. L. Dowe,J. J. Oliver,R. A. Baxter,C. S. Wallace作者: Ledger 時間: 2025-3-24 02:30
mbined with the choice of virtual topology and its corresponding packet routing in order to arrive at an optimum solution. Dynamic establishment and reconfiguration of lightpaths is an important issue which needs to be thoroughly studied. Some of these topics are studied in Chapters 9 and 7.作者: 招人嫉妒 時間: 2025-3-24 06:48
R. J. Hawkins,M. Rubinstein,G. J. Daniellmbined with the choice of virtual topology and its corresponding packet routing in order to arrive at an optimum solution. Dynamic establishment and reconfiguration of lightpaths is an important issue which needs to be thoroughly studied. Some of these topics are studied in Chapters 9 and 7.作者: GULLY 時間: 2025-3-24 10:46 作者: 哺乳動物 時間: 2025-3-24 16:05
Mike Westmbined with the choice of virtual topology and its corresponding packet routing in order to arrive at an optimum solution. Dynamic establishment and reconfiguration of lightpaths is an important issue which needs to be thoroughly studied. Some of these topics are studied in Chapters 9 and 7.作者: Compass 時間: 2025-3-24 22:24 作者: 內(nèi)行 時間: 2025-3-24 23:52 作者: Euthyroid 時間: 2025-3-25 05:19
Ali Mohammad-Djafariplacement, problems of optimal consumption of natural resources, and applications of control theory to economics. The book contains new results that were not available when the first edition was published, as well as an expansion of the material on stochastic optimal control theory..作者: 抱狗不敢前 時間: 2025-3-25 08:51
R. C. Puetterplacement, problems of optimal consumption of natural resources, and applications of control theory to economics. The book contains new results that were not available when the first edition was published, as well as an expansion of the material on stochastic optimal control theory..作者: dictator 時間: 2025-3-25 15:21 作者: Charlatan 時間: 2025-3-25 18:43
J. E. Gubernatis,J. Bon?a,Mark Jarrellplacement, problems of optimal consumption of natural resources, and applications of control theory to economics. The book contains new results that were not available when the first edition was published, as well as an expansion of the material on stochastic optimal control theory..作者: TRAWL 時間: 2025-3-25 23:54 作者: 猛然一拉 時間: 2025-3-26 04:06 作者: Insensate 時間: 2025-3-26 04:18 作者: nominal 時間: 2025-3-26 08:37
Bayesian Time Series: Models and Computations for the Analysis of Time Series in the Physical Scien sciences. With illustrations and references, we discuss: Bayesian inference and computation in various state-space models, with examples in analysing quasi-periodic series; isolation and modelling of various components of error in ime series; decompositions of time series into significant latent su作者: atopic 時間: 2025-3-26 14:04 作者: 魔鬼在游行 時間: 2025-3-26 17:12
Bayesian Estimation of the Von Mises Concentration Parameter, field of direction, μ, with concentration parameter, κ. The concentration parameter, κ, is the ratio of the field strength to the temperature of thermal fluctuations. Previously, we obtained a Bayesian estimator for the von Mises distribution parameters using the information-theoretic Minimum Messa作者: 主動 時間: 2025-3-26 22:42 作者: 闡明 時間: 2025-3-27 03:50 作者: SUE 時間: 2025-3-27 09:21
Mixture Modeling to Incorporate Meaningful Constraints into Learning, this difficult problem has been elaborated by both symbolic machine learning and neural networks communities. However, no fairly general methodology has emerged yet. The contribution of this paper is two-folded. First, we propose a Bayesian view of domain knowledge incorporation. In our framework t作者: Visual-Acuity 時間: 2025-3-27 10:08
Maximum Entropy (Maxent) Method in Expert Systems and Intelligent Control: New Possibilities and Liy, it is natural to use probabilities to describe uncertainty of the system’s answer to a given query Q. Since it is impossible to inquire about the expert’s probabilities for all possible (≥ 2.) propositional combinations of E.., a knowledge base is usually incomplete in the sense that there are ma作者: Guaff豪情痛飲 時間: 2025-3-27 15:05 作者: mortgage 時間: 2025-3-27 18:18
Continuum Models for Bayesian Image Matching,ential to the inference of the mapping because the image features on which matching is based are sparsely distributed and, consequently, underconstrain the problem. In this paper, we describe the Bayesian approach to image matching and introduce suitable priors based on idealized models of continua.作者: 織布機 時間: 2025-3-28 01:10 作者: 悅耳 時間: 2025-3-28 03:45 作者: 沖擊力 時間: 2025-3-28 09:05 作者: abnegate 時間: 2025-3-28 13:14 作者: Nerve-Block 時間: 2025-3-28 16:16
Bayesian Multimodal Evidence Computation by Adapti Tempering MCMC, the probability of the observed data given a model, the model evidence can be shown to be equal to the normalising constant of the underlying posterior distribution. When posterior unimodality is a valid assumption, it can be adequately evaluated using methods such as the Laplace method and standar作者: Dawdle 時間: 2025-3-28 21:21
to nationwide coverage. We showed that such a WDM-based network architecture can provide a high aggregate system capacity due to spatial reuse of wavelengths. Our objective was to investigate the overall design, analysis, upgradability, and optimization of a nationwide WDM network consistent with de作者: 做作 時間: 2025-3-29 02:07 作者: 誘騙 時間: 2025-3-29 06:01 作者: Bravura 時間: 2025-3-29 08:00 作者: pus840 時間: 2025-3-29 14:36 作者: Inculcate 時間: 2025-3-29 16:40 作者: 放逐 時間: 2025-3-29 21:13
K. M. Hanson,G. S. Cunningham the first edition, and expands coverage of stochastic optim.Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as a foundation for the book, which the authors have applied to business management problems developed from t作者: Implicit 時間: 2025-3-30 02:36 作者: Humble 時間: 2025-3-30 06:01 作者: 新奇 時間: 2025-3-30 08:19
Miao-Dan Wu,William J. Fitzgerald the first edition, and expands coverage of stochastic optim.Optimal control methods are used to determine optimal ways to control a dynamic system. The theoretical work in this field serves as a foundation for the book, which the authors have applied to business management problems developed from t作者: Loathe 時間: 2025-3-30 14:27 作者: 商議 時間: 2025-3-30 17:32 作者: DIS 時間: 2025-3-31 00:42
Reconstruction of the Probability Density Function Implicit in Option Prices from Incomplete and Nobility density function implicit in option prices from an incomplete and noisy set of option prices. We illustrate the potential of this approach by calculating the implied probability density function from observed S&P 500 index options.作者: 鉤針織物 時間: 2025-3-31 04:35
Hierarchical Bayesian Time Series Models,nalysis is then presented and discussed. Both discrete time and continuous time formulations are discussed. An brief overview of generalizations of the fundamental hierarchical time series model concludes the article.作者: 帶來墨水 時間: 2025-3-31 05:07
The Bootstrap is Inconsistent with Probability Theory, trials of some fixed size. It then proves that for no prior will the BS give the same first two moments as the predictive distribution for all size trials. It ends with an investigation of whether the BS can get the variance correct.作者: insightful 時間: 2025-3-31 12:39
Continuum Models for Bayesian Image Matching,ential to the inference of the mapping because the image features on which matching is based are sparsely distributed and, consequently, underconstrain the problem. In this paper, we describe the Bayesian approach to image matching and introduce suitable priors based on idealized models of continua.作者: demote 時間: 2025-3-31 15:50 作者: EVADE 時間: 2025-3-31 17:42
https://doi.org/10.1007/978-94-011-5430-7Fitting; Maximum entropy method; Measure; Probability theory; Statistical Physics; Time series; best fit; d作者: Delirium 時間: 2025-4-1 00:54
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks,We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks, a Dirichlet prior on the parameters is inevitable.作者: 是剝皮 時間: 2025-4-1 04:41
Bayesian Inference and the Analytic Continuation of Imaginary-Time Quantum Monte Carlo Data,We present a brief description of how methods of Bayesian inference are used to obtain real frequency information by the analytic continuation of imaginary-time quantum Monte Carlo data. We present the procedure we used, which is due to R. K. Bryan, and summarize several bottleneck issues.作者: 癡呆 時間: 2025-4-1 06:08
978-94-010-6284-8Kluwer Academic Publishers 1996作者: Working-Memory 時間: 2025-4-1 12:10
Maximum Entropy and Bayesian Methods978-94-011-5430-7Series ISSN 0168-1222 Series E-ISSN 2365-6425 作者: 受人支配 時間: 2025-4-1 18:21
0168-1222 Overview: 978-94-010-6284-8978-94-011-5430-7Series ISSN 0168-1222 Series E-ISSN 2365-6425
