Author : Ryan Martin
Publisher : CRC Press
Page : 274 pages
File Size : 24,77 MB
Release : 2015-09-25
Category : Mathematics
ISBN : 1439886512
A New Approach to Sound Statistical ReasoningInferential Models: Reasoning with Uncertainty introduces the authors' recently developed approach to inference: the inferential model (IM) framework. This logical framework for exact probabilistic inference does not require the user to input prior information. The authors show how an IM produces meaning
Author : James H. Stapleton
Publisher : John Wiley & Sons
Page : 466 pages
File Size : 40,67 MB
Release : 2007-12-14
Category : Mathematics
ISBN : 0470183403
This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readers Models for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chi-square, t, and F (central and non-central); the one- and two-sample Wilcoxon test, together with methods of estimation based on both; linear models with a linear space-projection approach; and logistic regression. Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package S-Plus(r) are included to help build the intuition of readers.
Author : Miquel A. Hernan
Publisher : CRC Press
Page : 352 pages
File Size : 34,8 MB
Release : 2019-07-07
Category : Medical
ISBN : 9781420076165
The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.
Author : Nick Heard
Publisher : Springer Nature
Page : 177 pages
File Size : 21,20 MB
Release : 2021-10-17
Category : Mathematics
ISBN : 3030828085
These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.
Author : Skyler J. Cranmer
Publisher : Cambridge University Press
Page : 317 pages
File Size : 25,68 MB
Release : 2020-11-19
Category : Business & Economics
ISBN : 1107158125
Pioneering introduction of unprecedented breadth and scope to inferential and statistical methods for network analysis.
Author : Ángel Nepomuceno-Fernández
Publisher : Springer Nature
Page : 510 pages
File Size : 19,1 MB
Release : 2019-10-24
Category : Philosophy
ISBN : 3030327221
This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning (MBR18), held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses issues concerning information visualization, experimental methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning.
Author : Miltiadis C. Mavrakakis
Publisher : CRC Press
Page : 444 pages
File Size : 17,84 MB
Release : 2021-03-28
Category : Mathematics
ISBN : 131536204X
Probability and Statistical Inference: From Basic Principles to Advanced Models covers aspects of probability, distribution theory, and inference that are fundamental to a proper understanding of data analysis and statistical modelling. It presents these topics in an accessible manner without sacrificing mathematical rigour, bridging the gap between the many excellent introductory books and the more advanced, graduate-level texts. The book introduces and explores techniques that are relevant to modern practitioners, while being respectful to the history of statistical inference. It seeks to provide a thorough grounding in both the theory and application of statistics, with even the more abstract parts placed in the context of a practical setting. Features: •Complete introduction to mathematical probability, random variables, and distribution theory. •Concise but broad account of statistical modelling, covering topics such as generalised linear models, survival analysis, time series, and random processes. •Extensive discussion of the key concepts in classical statistics (point estimation, interval estimation, hypothesis testing) and the main techniques in likelihood-based inference. •Detailed introduction to Bayesian statistics and associated topics. •Practical illustration of some of the main computational methods used in modern statistical inference (simulation, boostrap, MCMC). This book is for students who have already completed a first course in probability and statistics, and now wish to deepen and broaden their understanding of the subject. It can serve as a foundation for advanced undergraduate or postgraduate courses. Our aim is to challenge and excite the more mathematically able students, while providing explanations of statistical concepts that are more detailed and approachable than those in advanced texts. This book is also useful for data scientists, researchers, and other applied practitioners who want to understand the theory behind the statistical methods used in their fields.
Author : Daniel Chandler
Publisher : Oxford University Press
Page : 722 pages
File Size : 30,99 MB
Release : 2016-08-17
Category : Performing Arts
ISBN : 019105755X
The most accessible and up-to-date dictionary of its kind, this wide-ranging A-Z covers both interpersonal and mass communication, in all their myriad forms, encompassing advertising, digital culture, journalism, new media, telecommunications, and visual culture, among many other topics. This new edition includes over 200 new complete entries and revises hundreds of others, as well as including hundreds of new cross-references. The biographical appendix has also been fully cross-referenced to the rest of the text. This dictionary is an indispensable guide for undergraduate students on degree courses in media or communication studies, and also for those taking related subjects such as film studies, visual culture, and cultural studies.
Author : Deborah G. Mayo
Publisher : Cambridge University Press
Page : 503 pages
File Size : 42,9 MB
Release : 2018-09-20
Category : Mathematics
ISBN : 1108563309
Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.
Author : Sean Gailmard
Publisher : Cambridge University Press
Page : 393 pages
File Size : 18,49 MB
Release : 2014-06-09
Category : Business & Economics
ISBN : 1107003148
Written specifically for graduate students and practitioners beginning social science research, Statistical Modeling and Inference for Social Science covers the essential statistical tools, models and theories that make up the social scientist's toolkit. Assuming no prior knowledge of statistics, this textbook introduces students to probability theory, statistical inference and statistical modeling, and emphasizes the connection between statistical procedures and social science theory. Sean Gailmard develops core statistical theory as a set of tools to model and assess relationships between variables - the primary aim of social scientists - and demonstrates the ways in which social scientists express and test substantive theoretical arguments in various models. Chapter exercises guide students in applying concepts to data, extending their grasp of core theoretical concepts. Students will also gain the ability to create, read and critique statistical applications in their fields of interest.
