Preface. I. BASICS. 1. Introduction to Bayesian Networks. 2. More DAG/Probability Relationships. II. INFERENCE. 3. Inference: Discrete Variables. 4. More Inference Algorithms. 5. Influence Diagrams. III. LEARNING. 6. Parameter Learning: Binary Variables. 7. More Parameter Learning. 8. Bayesian Structure Learning. 9. Approximate Bayesian Structure Learning. 10. Constraint-Based Learning. 11. More Structure Learning. IV. APPICATIONS. 12. Applications. Bibliography. Index.

Learning Bayesian networks

Proponents of the Bayh-Dole Act argue that industrial use of federally funded research would be reduced without university patent licensing. Our survey of U.S. universities supports this view, emphasizing the embryonic state of most technologies licensed and the need for inventor cooperation in commercialization. Thus, for most university inventions, there is a moral-hazard problem with inventor effort. For such inventions, development does not occur unless the inventor's income is tied to the licensee's output by payments such as royalties or equity. Sponsored research from the licensee cannot by itself solve this problem.

/pdf/proofs-and-prototypes-for-sale-the-licensing-of-university-7omqrieq9y.pdf

Proofs and Prototypes for Sale: The Licensing of University Inventions

This book attempts to make growth models more accessible to foresters and others interested in mixed forests, whether planted or natural. There is an increasing interest in,
 and controversy surrounding the use of mixed plantations and natural forests, and rational discussion and resolution of management options require reliable growth models linked to other
 information systems. It is my hope that this book will help researchers to build better models, and will help users to understand how the models work and thus to appreciate their strengths
 and weaknesses. During recent years, vast areas of natural forest, especially in the tropics, have been logged or converted to other uses. Well-meaning forest managers have often been
 over-optimistic in estimating forest growth and yields, and this has contributed to over-cutting in some forests. Growth models can provide objective forecasts, offering forest managers the
 information needed to maintain harvests within the sustainable capacity of the forest, and providing quantitative data for land use planners to make informed decisions on land use
 alternatives. In this way, I hope that this book will contribute to the conservation and sustainable management of natural forests in the tropics and elsewhere. This is not a "How to do it"
 manual with step-by-step instructions to build a growth model for mixed forests. Unfortunately, modelling these forests isn't that easy. There is no single "best" way to build a model for
 these forests. Rather, many approaches can be used, and the best one depends on the data available, the time and expertise available to build the model, the computing resources, and the
 inferences that are to be drawn from the model. So instead of writing a "cookbook" with one or two recipes, I review and illustrate some of the many approaches available, indicate the
 requirements of and output from each, and highlight their strengths and limitations. The book emphasizes empirical-statistical models rather than physiological-process type models, not
 because they are superior, but because they have proven utility and offer immediate benefits for forest management. A more comprehensive treatment of all the options is beyond the scope of
 this book, which is intended to serve as a ready reference manual for those building growth models for forest management. Because of my limited linguistic ability, the material covered is
 more-or-less restricted to English-language material. I have not attempted to review all the published work on growth modelling (it would be a huge task), but have tried to highlight
 examples that may be applicable to mixed forests in tropical areas. I hope that the language and terminology used in this book will be accessible to all readers, especially those for whom
 English is a second language. The glossary may help to clarify some terms, and those that have a specific technical meaning are printed in italics the first time they are used. Readers
 should consult the glossary to clarify the meaning of these words unless they are sure of the meaning. Exercises are given at the end of each chapter to reinforce points made in the
 chapter. These are simple exercises, deliberately chosen so that they can be completed quickly with pen and paper or PC and spreadsheet, but within these constraints, I have tried to keep
 them realistic. Some exercises (e.g. 9.1 and 10.3) require more specialized statistical analyses, but many commercial statistical packages (e.g. GLIM) are suitable. Where possible, these
 exercises draw on real data, but some data were simulated to create interesting exercises with few data. Whilst my approach places more responsibility on the reader to choose and develop a
 suitable modelling methodology, I hope it will help readers gain a better understanding of modelling, which should in turn lead to better models and more reliable predictions. And I hope
 that better models will provide better information, greater understanding, and better management of mixed forests.

https://espace.library.uq.edu.au/view/UQ:8211/ModellingForestG.pdf

Modelling Forest Growth and Yield: Applications to Mixed Tropical Forests

Elicitation is a key task for subjectivist Bayesians. Although skeptics hold that elicitation cannot (or perhaps should not) be done, in practice it brings statisticians closer to their clients and subject-matter expert colleagues. This article reviews the state of the art, reflecting the experience of statisticians informed by the fruits of a long line of psychological research into how people represent uncertain information cognitively and how they respond to questions about that information. In a discussion of the elicitation process, the first issue to address is what it means for an elicitation to be successful; that is, what criteria should be used. Our answer is that a successful elicitation faithfully represents the opinion of the person being elicited. It is not necessarily “true” in some objectivistic sense, and cannot be judged in that way. We see that elicitation as simply part of the process of statistical modeling. Indeed, in a hierarchical model at which point the likelihood ends and the pr...

Statistical Methods for Eliciting Probability Distributions

Should one do a component analysis or a factor analysis? The choice is not obvious, because the two broad classes of procedures serve a similar purpose, and share many important mathematical characteristics. Despite many textbooks describing common factor analysis as the preferred procedure, principal component analysis has been the most widely applied. Here we summarize relevant information for the prospective factor/component analyst. First, we discuss the key algebraic similarities and differences. Next, we analyze a number of theoretical and practical issues. The more practical aspects include: the degree of numeric similarity between solutions from the two methods, some common rules for the number of factors to be retained, effects resulting from overextraction, problems with improper solutions, and comparisons in computational efficiency. Finally, we review some broader theoretical issues: the factor indeterminacy issue, the differences between exploratory and confirmatory procedures, and the issue ...

Component Analysis versus Common Factor Analysis: Some issues in Selecting an Appropriate Procedure.

We present a table of the Freeman-Tukey variance stabilizing arc-sine transformation for the binomial distribution together with properties of the transformation. Entries in the table are 

$$ \theta = \frac{1} {2}\left\{ {\arcsin \surd \left( {\frac{x} {{n + 1}}} \right) + \arcsin \surd \left( {\frac{{x + 1}} {{n + 1}}} \right)} \right\},$$

where n is the sample size and x is the number of successes observed in a binomial experiment. Values of θ are given in degrees, to two decimal places, for n = 1[1]50 and x = 0[1]n.

Tables of the Freeman-Tukey Transformations for the Binomial and Poisson Distributions

For 20 different studies, Table 1 tabulates numerical averages of opinions on quantitative meanings of 52 qualitative probabilistic expres- sions. Populations with differing occupations, mainly students, physicians, other medical workers, and science writers, contributed. In spite of the variety of populations, format of question, instructions, and context, the variation of the averages for most of the expressions was modest, suggesting that they might be useful for codification. One exception was possible, because it had distinctly different meanings for different people. We report new data from a survey of science writers. The effect of modifiers such as very or negation (not, un-, im-, in-) can be described approximately by a simple rule. The modified expression has probability meaning half as far from the appropriate boundary (0 or 100) as that of the original expression. This paper also reviews studies that show stability of meanings over 20 years, mild effects of translation into other languages, context, small order effects, and effects of scale for reporting on extreme values. The stem probability with modifiers gives a substantial range 6% to 91% and the stem chance might do as well if tried with very. The stems frequent, probable, likely, and often with modifiers produce roughly equivalent sets of means, but do not cover as wide a range as probability. Extreme values such as always and certain fall at 98% and 95%, respectively, and impossible and never at 1%. The next step will be to offer codifications and see how satisfactory people find them.

/pdf/quantifying-probabilistic-expressions-52kkmf0l66.pdf

Quantifying Probabilistic Expressions

The meanings of 18 verbal probability expressions were studied in 3 ways: (a) frequency distributions of what single number best represented each expression; (b) word-to-number acceptability functions from what range of numbers from 0% to 100% best represented each expression; and (c) number-to-word acceptability functions from which expressions were appropriate for multiples of 5% from 5% to 95%. These results agreed highly with others and were highly consistent across methods. Expressions incorporating the stem probable were quantitatively synonymous with expressions incorporating the stem likely. Except for expressions using the word chance, positive expressions (e.g., likely) were closer to 50% in meaning than corresponding negative expressions (e.g., unlikely). This method proved very useful in deriving fuzzy-set membership functions for probability words, encouraging us in our ongoing codification effort.

Quantitative meanings of verbal probability expressions

: Because little is known about properties of lines fitted by eye, we designed and carried out an empirical investigation. Inexperienced graduate and post-doctoral students instructed to locate a line for estimating y from x for four sets of points tended to choose slopes near that of the first principal component (major axis) of the data and their lines passed close to the centroids. Students had a slight tendency to choose consistently either steeper or shallower slopes for all sets of data. (Author)

http://www.dtic.mil/dtic/tr/fulltext/u2/a096716.pdf

Eye-Fitting of Straight Lines.

Because little is known about properties of lines fitted by eye, we designed and carried out an empirical investigation. Inexperienced graduate and postdoctoral students instructed to locate a line for estimating y from x for four sets of points tended to choose slopes near that of the first principal component (major axis) of the data, and their lines passed close to the centroids. Students had a slight tendency to choose consistently either steeper or shallower slopes for all sets of data.

Cleo Youtz

Papers

Tables of the Freeman-Tukey Transformations for the Binomial and Poisson Distributions

Quantifying Probabilistic Expressions

Quantitative meanings of verbal probability expressions

Eye-Fitting of Straight Lines.

Eye Fitting Straight Lines