Bernardo, J.M. et al. ed.
Bayesian Statistics 8
Oxford U.P. 2007.7
688 pp. (H)
ISBN 0-19-921465-4
27,200円
Contents
1 Generative or Discriminative? Getting the Best of Both Worlds / 2 Assessing the Effect of Genetic Mutation - A Bayesian Framework for Determining Population History from DNA Sequence Data / 3 Some Aspects of Bayesian Model Selection for Prediction / 4 Nonparametric Function Estimation Using Overcomplete Dictionaries / 5 Sequential Monte Carlo for Bayesian Computation / 6 Dynamic Gaussian Process Priors, with Applications to The Analysis of Space-time Data / 7 Bayesian Nonparametric Modelling for Spatial Data Using Dirichlet Processes / 8 Bayesian Nonparametric Latent Feature Models / 9 Objective Bayesian Analysis of Multiple Changepoints for Linear Models / 10 Bayesian Relaxation: Boosting / 11 The Bayesian Approach to the Analysis of Finite Population Surveys / 12 Detecting selection in DNA sequences: Bayesian Modelling and Inference / 13 Deriving Bayesian and frequentist estimators from time-invariance estimating equations: a unifying approach / 14 FDR and Bayesian Multiple Comparisons Rules / 15 Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity / 16 Approximating Interval Hypothesis: p-values and Bayes Factors / 17 Bayesian Probability in Quantum Mechanics / 18 Fast Bayesian Shape Matching Using Geometric Algorithms / 19 Nested Sampling for Bayesian Computations / 20 Objective Bayesian Analysis for the Multivariate Normal Model / *
Oaksford, M. & Chater, N.
Bayesian Rationality
The Probabilistic Approach to Human Reasoning
Oxford U.P. 2007.5
290 pp. (P)
ISBN 0-19-852449-8
8,200円
Contents
1. Logic and the Western conception of mind/ 2. Rationality and rational analysis/ 3. Reasoning in the the real world: how much deduction is there? / 4. The probabilistic turn/ 5. Does the exception prove the rule? How people reason with conditionals/ 6. Being economical with the evidence: collecting data and testing hypotheses/ 7. An uncertain quantity: how people reason with syllogisms/ 8. The rational analysis of mind: a dialogue/ *
* For almost two and a half thousand years, the Western conception of what it is to be a human being has been dominated by the idea that the mind is the seat of reason - humans are, almost by definition, the rational animal. From Aristotle to the present day, rationality has been explained by comparison to systems of logic, which distinguish valid (i.e., rationally justified) from invalid arguments. Within psychology and cognitive science, such a logicist conception of the mind was adopted wholeheartedly from Piaget onwards. Simultaneous with the construction of the logicist program in cognition, other researchers found that people appeared surprisingly and systematically illogical in some experiments. Proposals within the logicist paradigm suggested that these were mere performance errors, although in some reasoning tasks only as few as 5% of people's reasoning was logically correct. *
Koch, K.-R.
Introduction to Bayesian Statistics 2nd ed.
updated and enlarged edition
Springer-Verlag 2007.9
249 pp. (H)
ISBN 3-540-72723-X
15,800円
Contents
1 Introduction.- 2 Probability.- 3 Parameter Estimation, Confidence Regions and Hypothesis Testing.- 4 Linear Model.- 5 Special Models and Applications.- 6 Numerical Methods.- References.- Index. *
* The methods are applied to linear models, in models for a robust estimation, for prediction and filtering and in models for estimating variance components and covariance components. Regularization of inverse problems and pattern recognition are also covered while Bayesian networks serve for reaching decisions in systems with uncertainties. If analytical solutions cannot be derived, numerical algorithms are presented, such as the Monte Carlo integration and Markov Chain Monte Carlo methods. *
Smidl, V. & Quinn, A.
The Variational Bayes Method in Signal Processing
Springer-Verlag 2007.
227 pp. (H)
ISBN 3-540-28819-8
9,500円
Contents
1.Introduction.- 2.Bayesian Theory.- 3.Off-line Distributional Approximations and the Variational Bayes Method.- 4.Principal Component Analysis and Matrix Decompositions.- 5.Functional Analysis of Medical Image Sequences.- 6.On-line Inference of Time-Invariant Parameters.- 7.On-line Inference of Time-Variant Parameters.- 8.The Mixture-based Extension of the AR Model (MEAR).- 9.Concluding Remarks/ *
* This is the first book-length treatment of the Variational Bayes (VB) approximation in signal processing. It has been written as a self-contained, self-learning guide for academic and industrial research groups in signal processing, data analysis, machine learning, identification and control. It reviews the VB distributional approximation, showing that tractable algorithms for parametric model identification can be generated in off-line and on-line contexts. Many of the principles are first illustrated via easy-to-follow scalar decomposition problems. In later chapters, successful applications are found in factor analysis for medical image sequences, mixture model identification and speech reconstruction. Results with simulated and real data are presented in detail. The unique development of an eight-step "VB method", which can be followed in all cases, enables the reader to develop a VB inference algorithm from the ground up, for their own particular signal or image model. *
Hardle, W. & Simar, L.
Applied Multivariate Statistical Analysis 2nd ed.
Springer-Verlag 2007.
458 pp.(P)
ISBN 3-540-72243-2
12,900円
Contents
Part.I: Descriptive Techniques : 1. Comparison of Batches/ Part.II: Multivariate Random Variables : 2. A Short Excursion into Matrix Algebra/ 3. Moving to Higher Dimensions/ 4. Multivariate Distributions/ 5. Theory of the Multinormal/ 6. Theory of Estimation/ 7. Hypothesis Testing/ Part.III: Multivariate Techniques: 8. Decomposition of Data Matrices by Factors/ 9. Principal Components Analysis/ 10. Factor Analysis/ 11. Cluster Analysis/ 12. Discriminant Analysis/ 13. Correspondence Analysis/ 14. Canonical Correlation Analysis/ 15 Multidimensional Scaling/ 16 Conjoint Measurement Analysis/ 17 Applications in Finance/ 18 Computationally Intensive Techniques/ A. Symbols and Notations/ B. Data: B.1 Boston Housing Data/ B.2 Swiss Bank Notes/ B.3 Car Data/ B.4 Classic Blue Pullovers Data/ B.5 U.S. Companies Data/ B.6 French Food Data / B.7 CarMarks/ B.8 French Baccalaurｫeat Frequencies/ B.9 Journaux Data/ B.10 U.S. Crime Data/ B.11 Plasma Data/ B.12WAIS Data/ B.13ANOVA Data/ B.14 Timebudget Data/ B.15Geopol Data/ B.16 U.S. Health Data/ B.17 Vocabulary Data/ B.18Athletic Records Data/ B.19 Unemployment Data/ B.20 Annual Population Data/ B.21 Bankruptcy Data/ Bibliography/ Index/
* A key advantage of the work is its accessibility. This is because, in its focus on applications, the book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. In this second edition a wider scope of methods and applications of multivariate statistical analysis is introduced. All quantlets have been translated into the R and Matlab language and are made available online. *
Finkenstadt, B. et al. ed.
Statistical Methods of Spatio-Temporal Systems
Chapman & Hall/ CRC 2007.
286 pp. (H)
ISBN 1-58488-593-9
12,000円
Contents
1.Spatio-Temporal Point Processes: Methods and Applications/ 2.Spatio-Temporal Modelling - with a View to Biological Growth/ 3.Using Transforms to Analyze Space-Time Processes/ 4.Geostatistical Space-Time Models, Stationarity, Separability and Full Symmetry/ 5.Space-Time Modelling of Rainfall for Continuous Simulation/ 6.A Primer on Space-Time Modelling from a Bayesian Perspective/ Index/ *
962-10 登録日 08.01.20
