Probabilistic models have much to offer to philosophy. We continually receive information from a variety of sources: from our senses, from witnesses, from scientific instruments. When considering ...whether we should believe this information, we assess whether the sources are independent, how reliable they are, and how plausible and coherent the information is. Bovens and Hartmann provide a systematic Bayesian account of these features of reasoning. Simple Bayesian Networks allow us to model alternative assumptions about the nature of the information sources. Measurement of the coherence of information is a controversial matter: arguably, the more coherent a set of information is, the more confident we may be that its content is true, other things being equal. The authors offer a new treatment of coherence which respects this claim and shows its relevance to scientific theory choice. Bovens and Hartmann apply this methodology to a wide range of much discussed issues regarding evidence, testimony, scientific theories, and voting. Bayesian Epistemology is an essential tool for anyone working on probabilistic methods in philosophy, and has broad implications for many other disciplines. Available in OSO: http://www.oxfordscholarship.com/oso/public/content/philosophy/0199269750/toc.html
Together with the fundamentals of probability, random processes and statistical analysis, this insightful book also presents a broad range of advanced topics and applications. There is extensive ...coverage of Bayesian vs. frequentist statistics, time series and spectral representation, inequalities, bound and approximation, maximum-likelihood estimation and the expectation-maximization (EM) algorithm, geometric Brownian motion and Ito process. Applications such as hidden Markov models (HMM), the Viterbi, BCJR, and Baum-Welch algorithms, algorithms for machine learning, Wiener and Kalman filters, and queueing and loss networks are treated in detail. The book will be useful to students and researchers in such areas as communications, signal processing, networks, machine learning, bioinformatics, econometrics and mathematical finance. With a solutions manual, lecture slides, supplementary materials and MATLAB programs all available online, it is ideal for classroom teaching as well as a valuable reference for professionals.
Probability and Stochastics provides an introduction to the field, and begins by describing the fundamentals and basic principles of probability theory. Later chapters discuss more advanced topics, ...such as Martingales, Poisson Random Measures, Levy Processes and Brownian Motion.
Príspevok poukazuje na fundamentálnu diverzitu východísk súčasných filozoficko-edukačných teorií reflektujúcich sociokultúrnu mnohost’ a snaží sa pomocou troch teoretických modelov vyjadriť základné ...póly tejto diverzity. Tieto póly sú následne predmetom vzájomnej komparácie, čím sa ukážu ich teoretické obmedzenia, ale aj hranice ich edukačného realizovania.
Intuitive Probability and Random Processes using MATLABr is an introduction to probability and random processes that merges theory with practice. Based on the author's belief that only "hands-on" ...experience with the material can promote intuitive understanding, the approach is to motivate the need for theory using MATLAB examples, followed by theory and analysis, and finally descriptions of "real-world" examples to acquaint the reader with a wide variety of applications. The latter is intended to answer the usual question "Why do we have to study this?" Other salient features are:
*heavy reliance on computer simulation for illustration and student exercises
*the incorporation of MATLAB programs and code segments
*discussion of discrete random variables followed by continuous random variables to minimize confusion
*summary sections at the beginning of each chapter
*in-line equation explanations
*warnings on common errors and pitfalls
*over 750 problems designed to help the reader assimilate and extend the concepts
Intuitive Probability and Random Processes using MATLABr is intended for undergraduate and first-year graduate students in engineering. The practicing engineer as well as others having the appropriate mathematical background will also benefit from this book.
About the Author
Steven M. Kay is a Professor of Electrical Engineering at the University of Rhode Island and a leading expert in signal processing. He has received the Education Award "for outstanding contributions in education and in writing scholarly books and texts..." from the IEEE Signal Processing society and has been listed as among the 250 most cited researchers in the world in engineering.
This book presents elementary probability theory with interesting and well-chosen applications that illustrate the theory. An introductory chapter reviews the basic elements of differential calculus ...which are used in the material to follow.
V prispevku so predstavljeni rezultati nekaterih raziskav, opravljenih na področju izobraževanja in usposabljanja zaposlenih v manjših, srednjih in velikih slovenskih podjetjih. Raziskave kažejo na ...nekatere razlike v odnosu do razvoja zaposlenih kot dela poslovne strategije v podjetju, na ovire pri razvoju njihovih zmogljivosti, na povezave med zadovoljstvom z delom in motivacijo za izobraževanje. Prikazano je tudi, koliko je za vsebine, vezane z izobraževanjem in usposabljanjem, pomembno, ali je posameznik zaposlen v veli kem, srednjem ali manjšem podjetju.
Ovaj rad predstavlja glavne koncepte genetičkog algoritma (GA) u kombiniranom, edukativno-znanstvenom stilu. Svaki je korak GA isprva motiviran svojim biološkim uzorom, potom matematički formaliziran ...i objašnjen na jednostavnim primjerima te konačno potkrijepljen primjerima u MATLABU. Predstavljena su dva programa koja koris-te GA za ilustrativne slučajeve nalaženja ekstrema funkcija. Autori zaključuju članak prikazom izvorne uporabe GA u stohastičkoj iterativnoj dilemi zatvorenika, što je dalo novi pogled na ovaj problem.
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