Comparative Statistical Inference, Third Edition by Vic Barnett
English | PDF | 1999 | 400 Pages | ISBN : 0471976431 | 9.5 MB

English | 2021 | ISBN: 1648027989 | 487 pages | True PDF EPUB | 10.77 MB
In an age where we are inundated with information, the ability to discern verifiable information to make proper decisions and solve problems is ever more critical. Modern science, which espouses a systematic approach to making "inferences," requires a certain mindset that allows for a degree of comfort with uncertainty.

Niewiadomska M Probability and Statistical Inference 3ed 2021 | 123.99 MB
N/A | 585 Pages

Title: Probability and Statistical Inference 10th Edition
Author: Robert Hogg, Elliot Tanis, Dale Zimmerman
Year: 2011

English | 2022 | ISBN: 978-1032105710 | 391 pages | True PDF | 19.38 MB
The complexity of large-scale data sets ("Big Data") has stimulated the development of advanced

Aitkin M Introduction to Statistical Modelling Inference 2022 | 19.38 MB
N/A | 391 Pages

Title: Introduction to Statistical Modelling and Inference
Author: Murray Aitkin
Year: N/A

Behandeling van OCD:
Inclusief de Inference Based Approach: een nieuwe behandelmethode voor dwangklachten
Nederlands | 2022 | ISBN: 9036828309 | 424 Pages | PDF EPUB (True) | 8 MB

Hirschauer N Fundamentals of Statistical Inference 2022 | 2.67 MB
N/A | 141 Pages

Title: Super Duper Publications | Understanding Inferences Fun Deck | Problem Solving and Reasoning Skills Flash Cards | Educational Learning Materials for Children
Author: N/A
Year: N/A

Lio Y Bayesian Inference and Computation in Reliability 2022 | 8.2 MB
English | 367 Pages

Title: Bayesian Inference and Computation in Reliability and Survival Analysis (Emerging Topics in Statistics and Biostatistics)
Author: Yuhlong Lio
Year: 2022

Johannes Dellert, "Information-theoretic causal inference of lexical flow"
English | ISBN: 3961101442 | 2019 | 384 pages | PDF | 3 MB
This volume seeks to infer large phylogenetic networks from phonetically encoded lexical data and contribute in this way to the historical study of language varieties. The technical step that enables progress in this case is the use of causal inference algorithms. Sample sets of words from language varieties are preprocessed into automatically inferred cognate sets, and then modeled as information-theoretic variables based on an intuitive measure of cognate overlap. Causal inference is then applied to these variables in order to determine the existence and direction of influence among the varieties. The directed arcs in the resulting graph structures can be interpreted as reflecting the existence and directionality of lexical flow, a unified model which subsumes inheritance and borrowing as the two main ways of transmission that shape the basic lexicon of languages. A flow-based separation criterion and domain-specific directionality detection criteria are developed to make existing causal inference algorithms more robust against imperfect cognacy data, giving rise to two new algorithms. The Phylogenetic Lexical Flow Inference (PLFI) algorithm requires lexical features of proto-languages to be reconstructed in advance, but yields fully general phylogenetic networks, whereas the more complex Contact Lexical Flow Inference (CLFI) algorithm treats proto-languages as hidden common causes, and only returns hypotheses of historical contact situations between attested languages. The algorithms are evaluated both against a large lexical database of Northern Eurasia spanning many language families, and against simulated data generated by a new model of language contact that builds on the opening and closing of directional contact channels as primary evolutionary events. The algorithms are found to infer the existence of contacts very reliably, whereas the inference of directionality remains difficult. This currently limits the new algorithms to a role as exploratory tools fo

Social Inquiry and Bayesian Inference: Rethinking Qualitative Research
English | 2022 | ISBN: 1108421644 | 683 Pages | PDF | 7 MB
Fairfield and Charman provide a modern, rigorous and intuitive methodology for case-study research to help social scientists and analysts make better inferences from qualitative evidence. The book develops concrete guidelines for conducting inference to best explanation given incomplete information; no previous exposure to Bayesian analysis or specialized mathematical skills are needed. Topics covered include constructing rival hypotheses that are neither too simple nor overly complex, assessing the inferential weight of evidence, counteracting cognitive biases, selecting cases, and iterating between theory development, data collection, and analysis. Extensive worked examples apply Bayesian guidelines, showcasing both exemplars of intuitive Bayesian reasoning and departures from Bayesian principles in published case studies drawn from process-tracing, comparative, and multimethod research. Beyond improving inference and analytic transparency, an overarching goal of this book is to revalue qualitative research and place it on more equal footing with respect to quantitative and experimental traditions by illustrating that Bayesianism provides a universally applicable inferential framework.