THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data-parallel tasks, prior approaches are bottlenecked by algorithms that expose less parallelism, such as k-min selection, or make poor use of the memory hierarchy. 
We propose a design for k-selection that operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5x faster than prior GPU state of the art. We apply it in different similarity search scenarios, by proposing optimized design for brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation enables the construction of a high accuracy k-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.

Billion-scale similarity search with GPUs

Generalized Linear Models (2nd ed.)

https://bura.brunel.ac.uk/bitstream/2438/13152/4/Fulltext.pdf

Critical analysis of Big Data challenges and analytical methods

The book reviews section generally accepts for review only those books whose content and level reflect the general editorial policy of Technometrics. Publishers are invited to send books for review to Eric R. Ziegel, Amoco Research Center, Mail Station F-l/C& P.O. Box 3011, Naperville, Illinois 60566-7011. Please include the price of the book. The opinions expressed in this section are those of the reviewers and do not necessarily reflect those of the editorial staff or the sponsoring societies. Listed prices were provided by the publisher when the books were received by Technometrics and may not be current.

Adventures in Stochastic Processes

We describe a simulation-time random sampling of a large-scale particle simulation, the RoadRunner Universe MC3 cosmological simulation, for interactive post-analysis and visualization. Simulation data generation rates will continue to be far greater than storage bandwidth rates by many orders of magnitude. This implies that only a very small fraction of data generated by a simulation can ever be stored and subsequently post-analyzed. The limiting factors in this situation are similar to the problem in many population surveys: there aren't enough human resources to query a large population. To cope with the lack of resources, statistical sampling techniques are used to create a representative data set of a large population. Following this analogy, we propose to store a simulationtime random sampling of the particle data for post-analysis, with level-of-detail organization, to cope with the bottlenecks. A sample is stored directly from the simulation in a level-of-detail format for post-visualization and analysis, which amortizes the cost of post-processing and reduces workflow time. Additionally by sampling during the simulation, we are able to analyze the entire particle population to record full population statistics and quantify sample error.

/pdf/in-situ-sampling-of-a-large-scale-particle-simulation-for-j5mw64umxu.pdf

In-situ sampling of a large-scale particle simulation for interactive visualization and analysis

Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are often used to assess the shelf life of materials, components, and products. We show how to evaluate the properties of proposed test plans. Such evaluations are needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the related lifetime distribution are to be estimated. We use the approximate large-sample variance–covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods ...

https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=2965&context=etd

Methods for Planning Repeated Measures Degradation Studies

We consider Bayesian prediction intervals that contain a proportion of a finite number of observations with a specified probability. Such intervals arise in numerous applied contexts and are closely related to tolerance intervals. Several examples are provided to illustrate this methodology, and simulation studies are used to demonstrate potential pitfalls of using tolerance intervals when prediction intervals are required.

Bayesian Prediction Intervals and Their Relationship to Tolerance Intervals

Repeated measures degradation studies are used to assess product or component reliability when there are few or even no failures expected during a study. Such studies are often used to assess the shelf life of materials, components, and products. We show how to evaluate the properties of proposed test plans. Such evaluations are needed to identify statistically efficient tests. We consider test plans for applications where parameters related to the degradation distribution or the related lifetime distribution are to be estimated. We use the approximate large-sample variance–covariance matrix of the parameters of a mixed effects linear regression model for repeated measures degradation data to assess the effect of sample size (number of units and number of measurements within the units) on estimation precision of both degradation and failure-time distribution quantiles. We also illustrate the complementary use of simulation-based methods for evaluating and comparing test plans. These test-planning methods are illustrated with two examples. We provide the R code and examples as supplementary materials (available online on the journal web site) for this article.

Joanne Wendelberger

Papers

Adventures in Stochastic Processes

In-situ sampling of a large-scale particle simulation for interactive visualization and analysis

Methods for Planning Repeated Measures Degradation Studies

Bayesian Prediction Intervals and Their Relationship to Tolerance Intervals

Methods for planning repeated measures degradation studies