182,511 research outputs found
Methods for field measurement and remote sensing of the swash zone
No full textSwash action is the dominant process responsible for the cross-shore exchange of sediment between the subaerial and subaqueous zones, with a significant part of the littoral drift also taking place as a result of swash motions. The swash zone is the area of the beach between the inner surfzone and backbeach that is intermittently submerged and exposed by the processes of wave uprush and backwash. Given the dominant role that swash plays in the morphological evolution of a beach, it is important to understand and quantify the main processes. The extent of swash (horizontally and vertically), current velocities and suspended sediment concentrations are all parameters of interest in the study of swash processes. In situ methods of measurements in this energetic zone were instrumental in developing early understanding of swash processes, however, the field has experienced a shift towards remote sensing methods. This article outlines the emergence of high precision technologies such as video imaging and LIDAR (light detection and ranging) for the study of swash processes. Furthermore, the applicability of these methods to large-scale datasets for quantitative analysis is demonstrated
Jean Pitman, 1953 Sophomore
No full textJean Pitman was a sophomore at Jacksonville State Teachers College in 1952-1953.https://digitalcommons.jsu.edu/lib-ac-histimg/6706/thumbnail.jp
Importance conditional sampling for Pitman-Yor mixtures
No full textNonparametric mixture models based on the Pitman–Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the number of components characterizing the distribution of the data. We propose a new sampling strategy for such models, named importance conditional sampling (ICS), which combines appealing properties of existing methods, including easy interpretability and a within-iteration parallelizable structure. An extensive simulation study highlights the efficiency of the proposed method which, unlike other conditional samplers, shows stable performances for different specifications of the parameters characterizing the Pitman–Yor process. We further show that the ICS approach can be naturally extended to other classes of computationally demanding models, such as nonparametric mixture models for partially exchangeable data
Humanities Building with Morris R. and Mavis C. Pitman Tower, Rice University
No full textA view of the Humanities Building and the Morris R. and Mavis C. Pitman tower. The picture was taken so the clock tower was prominent. Original resource is a color photograph
David Kyle Pitman, ca. 1860\u27s,
No full textCarte de visite of David Kyle Pitman, hand-colorized, ca. 1860\u27s, b&w. Note on back: David K. Pitman, David Kyle Pitman. Backprint of R. Goebel, Photographer, St. Charles, Mo. Cheeks have been hand-colorized (This is from the Carte de visite album in folder 17.) Rudolph Goebel was born Germany, 1835. By 1856 he was producing dageureotypes in St. Charles. During the Civil War, his studio was located opposite the Courthouse. He had a large exhibit at the 1870 St. Louis Fair, and produced a picture book of St. charles in 1872. Was working at least into the 1880\u27s.https://mds.marshall.edu/dorothy_atkins_papers/1023/thumbnail.jp
The Pitman nearness criterion and its determination
No full textA general method for determining Pitman Nearness is given In the case of univariate estimators. This method is then applied to some estimation problems. The concept of Pitman Nearness is also generalized to the multivariate case. The James-Stein estimators are used to illustrate the multivariate comparison
Book, Encyclopedia of Murder by Colin Wilson and Pat Pitman
No full textColor slide photograph of a display about a book titled "Encyclopedia of Murde,r" by Colin Wilson and Pat Pitman
Exponentielle Familien, Suffizienz und das Theorem von Darmois-Koopman-Pitman
No full textIm Mittelpunkt dieser Arbeit steht die Analyse des klassischen Satzes von (Fisher-)Darmois-Koopman-Pitman. Drei Arbeiten von G. Darmois, B. O. Koopman und E. J. G. Pitman aus den Jahren 1935/36 haben diesen Satz unabhängig voneinander, aufbauend auf Arbeiten R. A. Fishers, publiziert. Die vorliegende Arbeit analysiert diese drei klassischen Arbeiten. Dabei wird deren Inhalt dargestellt und mit der statistischen Theorie aus heutiger Sicht verglichen. Zudem erfolgt ein Abriss der Theorie aus heutiger Sicht. Neben grundlegenden Begriffen der Maß- und Wahrscheinlichkeitstheorie werden statistische Grundlagen präsentiert. Exponentielle Verteilungsfamilien sowie suffiziente Statistiken werden ausführlich dargestellt. Des Weiteren erfolgt ein Abriss der Punktschätzung hinsichtlich der Begriffe Erwartungstreue, Fisher-Information und Maximum-Likelihood. Den Hauptteil der Arbeit bildet die Analyse der Arbeiten von G. Darmois, B. O. Koopman und E. J. G. Pitman, sowie der Vorarbeit von R. A. Fisher.This diploma thesis deals with the classical theorem of (Fisher-)Darmois-Koopman-Pitman. Three papers by G. Darmois, B. O. Koopman and E. J. G. Pitman from 1935/36 have published this theorem independently of one another, building on the works of R. A. Fisher. The present work analyzes these three classical works. Their content is presented and compared with contempoary statistical theory. An overview of the theory takes place from today’s perspective. In addition to basic concepts of measurement and probability theory, statistical foundations are presented. Exponential families as well as sufficient statistics are presented in detail. In addition, a brief presentation of point estimation is given in terms of expectation, Fisher information,
and maximum likelihood. The main part of the diploma thesis is the analysis of the work of G. Darmois, B. O. Koopman and E. J. G. Pitman, as well as the preliminary work of R. A. Fisher
Annie Pitman Glanville, ca. 1860\u27s
No full textCarte de visite of Annie Pitman Glanville, ca. 1860\u27s, col,. Note on back: Annie Pitman Glanville backprint: R. Goebel, photographer, St. Charles, Mo Rudolph Goebel was born Germany, 1835. By 1856 he was producing dageureotypes in St. Charles. During the Civil War, his studio was located opposite the Courthouse. He had a large exhibit at the 1870 St. Louis Fair, and produced a picture book of St. charles in 1872. Was working at least into the 1880\u27s. (This is from the Carte de visite album in folder 17.).https://mds.marshall.edu/dorothy_atkins_papers/1027/thumbnail.jp
Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference
No full textMultiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non–parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process mixture approach and define a new class of multivariate dependent Pitman-Yor processes (DPY). The proposed DPY are represented in terms of a vector of stickbreaking processes which determines dependent clustering structures in the time series. We
follow a hierarchical specification of the DPY base measure to accounts for various degrees of information pooling across the series. We discuss some theoretical properties of the DPY and use them to define Bayesian non parametric repeated measurement and vector autoregressive models. We provide efficient Monte Carlo Markov Chain algorithms for posterior computation of the proposed models and illustrate the effectiveness of the method with a simulation study and an application to the United States and the European Union business cycles
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