This paper reviews the literature on Bayesian experimental design. A unified view of this topic is presented, based on a decision-theoretic approach. This framework casts criteria from the Bayesian literature of design as part of a single coherent approach. The decision-theoretic structure incorporates both linear and nonlinear design problems and it suggests possible new directions to the experimental design problem, motivated by the use of new utility functions. We show that, in some special cases of linear design problems, Bayesian solutions change in a sensible way when the prior distribution and the utility function are modified to allow for the specific structure of the experiment. The decision-theoretic approach also gives a mathematical justification for selecting the appropriate optimality criterion.

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Bayesian Experimental Design: A Review

Subjectivism has become the dominant philosophical foundation for Bayesian inference. Yet in practice, most Bayesian analyses are performed with so-called “noninformative” priors, that is, priors constructed by some formal rule. We review the plethora of techniques for constructing such priors and discuss some of the practical and philosophical issues that arise when they are used. We give special emphasis to Jeffreys's rules and discuss the evolution of his viewpoint about the interpretation of priors, away from unique representation of ignorance toward the notion that they should be chosen by convention. We conclude that the problems raised by the research on priors chosen by formal rules are serious and may not be dismissed lightly: When sample sizes are small (relative to the number of parameters being estimated), it is dangerous to put faith in any “default” solution; but when asymptotics take over, Jeffreys's rules and their variants remain reasonable choices. We also provide an annotated b...

The Selection of Prior Distributions by Formal Rules

This chapter provides an overview of recent developments in the design and analysis of cross-over trials. We first consider the analysis of the trial that compares two treatments, A and B, over two periods and where the subjects are randomized to the treatment sequences AB and BA. We make the distinction between fixed and random effects models and show how these models can easily be fitted using modern software. Issues with fitting and testing for a difference in carry-over effects are described and the use of baseline measurements is discussed. Simple methods for testing for a treatment difference when the data are binary are also described. Various designs with two or more treatments but with three or four periods are then described and compared. These include the balanced and partially balanced designs for three or more treatments and designs for factorial treatment combinations. Also described are nearly balanced and nearly strongly balanced designs. Random subject-effects models for the designs with two or more treatments are described and methods for analysing non-normal data are also given. The chapter concludes with a description of the use of cross-over designs in the testing of bioequivalence.

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Design and Analysis of Cross-Over Trials

Maximizing data information requires careful selection, termed design, of the points at which data are observed. Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Experimental design

A uniform design (UD) seeks design points that are uniformly scattered on the domain. It has been popular since 1980. A survey of UD is given in the first portion: The fundamental idea and construction method are presented and discussed and examples are given for illustration. It is shown that UD's have many desirable properties for a wide variety of applications. Furthermore, we use the global optimization algorithm, threshold accepting, to generate UD's with low discrepancy. The relationship between uniformity and orthogonality is investigated. It turns out that most UD's obtained here are indeed orthogonal.

Uniform Design: Theory and Application

Offering a concise account of the most appropriate and efficient procedures for analyzing data from queries dealing with sensitive and confidential issues- including the first book-length treatment of infinite and finite population set-ups - this volume begins with the simplest problems, complete with their properties and solutions, and proceeds to incrementally more difficult topics. Randomized Response is mandatory reading for statisticians and biostatisticians, market researchers, operations researchers, pollsters, sociologists, political scientists, economists and advanced undergraduate and graduate students in these areas.

Randomized Response : Theory and Techniques

Fractional Plans and Orthogonal Arrays. Symmetric Orthogonal Arrays. Asymmetric Orthogonal Arrays. Some Results on Nonexistence. More on Optimal Fractional Plans and Related Topics. Trend-Free Plans and Blocking. Some Further Developments. Appendix. References. Index.

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Fractional Factorial Plans

Introduction and the Shrinkage Argument.- Matching Priors for Posterior Quantiles.- Matching Priors for Distribution Functions.- Matching Priors for Highest Posterior Density Regions.- Matching Priors for Other Credible Regions.- Matching Priors for Prediction.

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Probability Matching Priors: Higher Order Asymptotics

The last twenty years have witnessed a significant growth of interest in optimal factorial designs, under possible model uncertainty, via the minimum aberration and related criteria. This book gives, for the first time in book form, a comprehensive and up-to-date account of this modern theory. Many major classes of designs are covered in the book. While maintaining a high level of mathematical rigor, it also provides extensive design tables for research and practical purposes. Apart from being useful to researchers and practitioners, the book can form the core of a graduate level course in experimental design.

A modern theory of factorial designs

In this article, based on the overview of network coupling structure between radio access technologies, the concept of joint radio resource management built onto the reference structure is introduced. In order to optimize usage of radio resource and jointly designed from the user perspective, a joint scheduling mechanism allowing traffic to be split over a tightly coupled radio network supported by an adaptive radio multihoming approach is deliberately discussed. With respect to the time-division access scheme in HIPERLAN/2, which is selected as one example of WLAN, algorithms and performance of traffic scheduling in such a radio access technology are given. The required synchronization scheme supporting traffic splitting is also introduced.

Rahul Mukerjee

Papers

Randomized Response : Theory and Techniques

Fractional Factorial Plans

Probability Matching Priors: Higher Order Asymptotics

A modern theory of factorial designs

Investigation of radio resource scheduling in WLANs coupled with 3G cellular network