Edwin B. Wilson

Bio: Edwin B. Wilson is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Theory of relativity. The author has an hindex of 24, co-authored 142 publications receiving 6408 citations. Previous affiliations of Edwin B. Wilson include Massachusetts Institute of Technology & University of Glasgow.

Probable Inference, the Law of Succession, and Statistical Inference

Abstract: (1927). Probable Inference, the Law of Succession, and Statistical Inference. Journal of the American Statistical Association: Vol. 22, No. 158, pp. 209-212.

The Purchasing Power of Money; Its Determination and Relation to Credit, Interest and Crises

Abstract: RareBooksClub. Paperback. Book Condition: New. This item is printed on demand. Paperback. 142 pages. Dimensions: 9.7in. x 7.4in. x 0.3in.This historic book may have numerous typos and missing text. Purchasers can usually download a free scanned copy of the original book (without typos) from the publisher. Not indexed. Not illustrated. 1920 edition. Excerpt: . . . for by the increasing volume of trade and the successive demonetization of silver by various countries. The foregoing parallelism between monetary stocks and prices is somewhat remarkable in view of the incompleteness of the data. 1 In the table there are lacking, not only exact statistics as to the volume of trade and all statistics whatever of velocity of circulation, but also statistics of the volume of bank notes, government notes, and deposit currency. We know, however, that modern banking, which had scarcely developed at all before the French Revolution, developed rapidly throughout the nineteenth century. It is also known that banking and deposit currency developed more rapidly during the third period in the table (1849-1873) than during the fourth (1873-1896), 2 which fact contributes somewhat to explain the contrast between the price movements of these two periods. 4 We may, therefore, summarize the...

Regression Models and Life-Tables

Abstract: The analysis of censored failure times is considered. It is assumed that on each individual arc available values of one or more explanatory variables. The hazard function (age-specific failure rate) is taken to be a function of the explanatory variables and unknown regression coefficients multiplied by an arbitrary and unknown function of time. A conditional likelihood is obtained, leading to inferences about the unknown regression coefficients. Some generalizations are outlined.

Classification and regression trees

Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

Quantum mechanical continuum solvation models.

Abstract: 6.2.2. Definition of Effective Properties 3064 6.3. Response Properties to Magnetic Fields 3066 6.3.1. Nuclear Shielding 3066 6.3.2. Indirect Spin−Spin Coupling 3067 6.3.3. EPR Parameters 3068 6.4. Properties of Chiral Systems 3069 6.4.1. Electronic Circular Dichroism (ECD) 3069 6.4.2. Optical Rotation (OR) 3069 6.4.3. VCD and VROA 3070 7. Continuum and Discrete Models 3071 7.1. Continuum Methods within MD and MC Simulations 3072

Bank Runs, Deposit Insurance, and Liquidity

Abstract: This paper shows that bank deposit contracts can provide allocations superior to those of exchange markets, offering an explanation of how banks subject to runs can attract deposits. Investors face privately observed risks which lead to a demand for liquidity. Traditional demand deposit contracts which provide liquidity have multiple equilibria, one of which is a bank run. Bank runs in the model cause real economic damage, rather than simply reflecting other problems. Contracts which can prevent runs are studied, and the analysis shows that there are circumstances when government provision of deposit insurance can produce superior contracts.

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Abstract: We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning. Our approach is derived from a set of assumptions made previously as well as the assumption of likelihood equivalence, which says that data should not help to discriminate network structures that represent the same assertions of conditional independence. We show that likelihood equivalence when combined with previously made assumptions implies that the user's priors for network parameters can be encoded in a single Bayesian network for the next case to be seen—a prior network—and a single measure of confidence for that network. Second, using these priors, we show how to compute the relative posterior probabilities of network structures given data. Third, we describe search methods for identifying network structures with high posterior probabilities. We describe polynomial algorithms for finding the highest-scoring network structures in the special case where every node has at most k e 1 parent. For the general case (k > 1), which is NP-hard, we review heuristic search algorithms including local search, iterative local search, and simulated annealing. Finally, we describe a methodology for evaluating Bayesian-network learning algorithms, and apply this approach to a comparison of various approaches.