ISBN: 9781334538254 - a fortiori bayesian inference in psychological research (classic reprint) (3 risultati)

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  • Lingua: Inglese

    Editore: Forgotten Books, 2018

    1334538255 / 9781334538254

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    Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US

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    EUR 27,12

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    Quantità: 15 disponibili

    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

  • Lingua: Inglese

    Editore: Forgotten Books, 2018

    1334538255 / 9781334538254

    • Brossura

    Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK

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    Condizione: Nuovo

    EUR 26,20

    EUR 3,95 spedizione 
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    Quantità: 15 disponibili

    PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

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    Lingua: Inglese

    Editore: Forgotten Books, 2018

    1334538255 / 9781334538254

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    • Print on Demand

    Da: Forgotten Books, London, Regno UnitoForgotten Books

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    Condizione: Nuovo

    EUR 17,09

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    Quantità: Più di 20 disponibili

    Paperback. Condizione: New. Print on Demand. This book, written by a leading expert in the field, presents an innovative approach to the analysis and interpretation of psychological data using Bayesian theory. Bayesian inference provides a natural framework for hypothesis testing, particularly when the goal is to estimate the strength of a relationship rather than to simply reject the null hypothesis. The author proposes a very conservative way to estimate the strength of a relationship by constraining the prior mean to lie at the conventional null point, and then selecting prior parameters which maximize the likelihood. Such a prior has two interesting features. First, because the argument of the prior is a continuous random variable, the either-or point null is discarded in favor of a âgrayâ diffuse null, whose expectation is the conventional null value of the parameter. Second, by choosing the prior parameters to maximize the constrained likelihood, a null centered prior is defined which has the maximum probability of generating the observed sample data. For a given functional form, the resulting prior stacks the cards against the research hypothesis as much as possible. The inference problem is transformed into computing the probabilistic description of the output parameter, given the sample data. Typically, a random variable is described in terms of the probability that it lies in a small interval. The author develops a variety of tests and illustrates the techniques with numerical examples. This book's insights on applying Bayesian inference to psychological contexts will appeal to advanced undergraduate and graduate students, as well as researchers, in psychology and related fields interested in hypothesis testing and parameter estimation. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item.…