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Produktbild: Lambert, B: Student's Guide to Bayesian Statistics
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Lambert, B: Student's Guide to Bayesian Statistics

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.05.2018

Verlag

Sage Publications

Seitenzahl

498

Maße (L/B/H)

25,1/19,5/3,8 cm

Gewicht

1548 g

Sprache

Englisch

ISBN

978-1-4739-1635-7

Beschreibung

Rezension

An excellent resource on Bayesian analysis accessible to students from a diverse range of statistical backgrounds and interests.  Easy to follow with well documented examples to illustrate key concepts.
Bronwyn Loong 20170619

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

04.05.2018

Verlag

Sage Publications

Seitenzahl

498

Maße (L/B/H)

25,1/19,5/3,8 cm

Gewicht

1548 g

Sprache

Englisch

ISBN

978-1-4739-1635-7

EU-Ansprechpartner

Zeitfracht Medien GmbH
Ferdinand-Jühlke-Straße 7
99095 Erfurt
DE

Herstelleradresse

SAGE Publications
1 Oliver's Yard 55 City Road
EC1Y 1SP London
GB

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Die Leseprobe wird geladen.
  • Produktbild: Lambert, B: Student's Guide to Bayesian Statistics
  • Chapter 1: How to best use this book
    The purpose of this book
    Who is this book for?
    Pre-requisites
    Book outline
    Route planner - suggested journeys through Bayesland
    Video
    Problem sets
    Code
    R and Stan
    Why don't more people use Bayesian statistics?
    What are the tangible (non-academic) benefits of Bayesian statistics?
    Part I: An introduction to Bayesian inference
    Chapter 2: The subjective worlds of Frequentist and Bayesian statistics
    Bayes' rule - allowing us to go from the effect back to its cause
    The purpose of statistical inference
    The world according to Frequentists
    The world according to Bayesians
    Do parameters actually exist and have a point value?
    Frequentist and Bayesian inference
    Bayesian inference via Bayes' rule
    Implicit versus Explicit subjectivity
    Chapter 3: Probability - the nuts and bolts of Bayesian inference
    Probability distributions: helping us explicitly state our ignorance
    Independence
    Central Limit Theorems
    A derivation of Bayes' rule
    The Bayesian inference process from the Bayesian formula
    Part II: Understanding the Bayesian formula
    Chapter 4: Likelihoods
    What is a likelihood?
    Why use 'likelihood' rather than 'probability'?
    What are models and why do we need them?
    How to choose an appropriate likelihood?
    Exchangeability vs random sampling
    Maximum likelihood - a short introduction
    Chapter 5: Priors
    What are priors, and what do they represent?
    The explicit subjectivity of priors
    Combining a prior and likelihood to form a posterior
    Constructing priors
    A strong model is less sensitive to prior choice
    Chapter 6: The devil's in the denominator
    An introduction to the denominator
    The difficulty with the denominator
    How to dispense with the difficulty: Bayesian computation
    Chapter 7: The posterior - the goal of Bayesian inference
    Expressing parameter uncertainty in posteriors
    Bayesian statistics: updating our pre-data uncertainty
    The intuition behind Bayes' rule for inference
    Point parameter estimates
    Intervals of uncertainty
    From posterior to predictions by sampling
    Part III: Analytic Bayesian methods
    Chapter 8: An introduction to distributions for the mathematically-un-inclined
    The interrelation among distributions
    Sampling distributions for likelihoods
    Prior distributions
    How to choose a likelihood
    Table of common likelihoods, their uses, and reasonable priors
    Distributions of distributions, and mixtures - link to website, and relevance
    Chapter 9: Conjugate priors and their place in Bayesian analysis
    What is a conjugate prior and why are they useful?
    Gamma-poisson example
    Normal example: giraffe height
    Table of conjugate priors
    The lessons and limits of a conjugate analysis
    Chapter 10: Evaluation of model fit and hypothesis testing
    Posterior predictive checks
    Why do we call it a p value?
    Statistics measuring predictive accuracy: AIC, Deviance, WAIC and LOO-CV
    Marginal likelihoods and Bayes factors
    Choosing one model, or a number?
    Sensitivity analysis
    Chapter 11: Making Bayesian analysis objective?
    The illusion of the 'uninformative' uniform prior
    Jeffreys' priors
    Reference priors
    Empirical Bayes
    A move towards weakly informative priors
    Part IV: A practical guide to doing real life Bayesian analysis: Computational Bayes
    Chapter 12: Leaving conjugates behind: Markov Chain Monte Carlo
    The difficulty with real life Bayesian inference
    Discrete approximation to continuous posteriors
    The posterior through quadrature
    Integrating using independent samples: an introduction to Monte Carlo
    Why is independent sampling easier said than done?
    Ideal sampling from a posterior using only the un-normalised posterior
    Moving from independent to dependent sampling
    What's the catch with dependent samplers?
    Chapter 13: Random Walk Metropolis
    Sustainable fishing
    Prospecting for gold
    Defining the Metropolis algorithm
    When does Metropolis work?
    Efficiency of convergence: the importance of choosing the right proposal scale
    Metropolis-Hastings
    Judging convergence
    Effective sample size revisited
    Chapter 14: Gibbs sampling
    Back to prospecting for gold
    Defining the Gibbs algorithm
    Gibbs' earth: the intuition behind the Gibbs algorithm
    The benefits and problems with Gibbs and Random Walk Metropolis
    A change of parameters to speed up exploration
    Chapter 15: Hamiltonian Monte Carlo
    Hamiltonian Monte Carlo as a sledge
    NLP space
    Solving for the sledge motion over NLP space
    How to shove the sledge
    The acceptance probability of HMC
    The complete Hamiltonian Monte Carlo algorithm
    The performance of HMC versus Random Walk Metropolis and Gibbs
    Optimal step length of HMC: introducing the "No U-Turn Sampler"
    Chapter 16: Stan
    Why Stan, and how to get it
    Getting setup with Stan using RStan
    Our first words in Stan
    Essential Stan reading
    What to do when things go wrong
    How to get further help
    Part V: Hierarchical models and regression
    Chapter 17: Hierarchical models
    The spectrum from fully-pooled to heterogeneous
    Non-centered parameterisations in hierarchical models
    Case study: Forecasting the EU referendum result
    The importance of fake data simulation for complex models
    Chapter 18: Linear regression models
    Example: high school test scores in England
    Pooled model
    Interactions
    Heterogeneous coefficient model
    Hierarchical model
    Incorporating LEA-level data
    Chapter 19: Generalised linear models and other animals
    Example: electoral participation in European countries
    Discrete parameter models in Stan