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Handbook of Approximate Bayesian Computation
Taschenbuch von Mark Beaumont (u. a.)

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Beschreibung

The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.

The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.

Über den Autor

Scott Sission is Professor, ARC Future Fellow and Head of Statistics in the School of Mathematics and Statistics at UNSW.

Yanan Fan is a Senior Lecturer at the School of Mathematics and Statistics at UNSW.

Mark Beaumont is Professor of Statistics at the University of Bristol.

Inhaltsverzeichnis

Introduction

Overview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. Beaumont

On the history of ABC: S.Tavare

Regression approaches: M. G. B. Blum

Monte Carlo samplers for ABC: Y. Fan and S. A. Sisson

Summary statistics: D. Prangle

Likelihood-free model choose: J.-M. Marin, P. Pudlo, A. Estoup and C. Robert

ABC and indirect inference: C. C. Drovandi

High-dimensional ABC: D. Nott, V. Ong, Y. Fan and S. A. Sisson Theoretical and methodological aspects of MCMC computations with noisy likelihoods: C. Andrieu, A.Lee and M. Viola

Informed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. Coljin

Approximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. Robert

Software: D.Wegmann

Divide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. Cottet

SMC-ABC methods for estimation of stochastic simulation models of the limit order book: G.W. Peters, E. Panayi and F. Septier

Inferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data: G. S. Rodrigues, S. A. Sisson, M. M. Tanaka

ABC in Systems Biology: J. Liepe and M. P. H. Stumpf

Application of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et al

ABC for climate: dealing with expensive simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. Wilkinson

ABC in ecological modelling: M. Fasiolo and S. N. Wood

ABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek

Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 680
ISBN-13: 9780367733728
ISBN-10: 0367733722
Einband: Kartoniert / Broschiert
Redaktion: Beaumont, Mark
Sisson, Scott A.
Fan, Yanan
Hersteller: Taylor & Francis Ltd
Maße: 156 x 234 x 42 mm
Von/Mit: Mark Beaumont (u. a.)
Erscheinungsdatum: 18.12.2020
Gewicht: 1,04 kg
preigu-id: 127165626
Über den Autor

Scott Sission is Professor, ARC Future Fellow and Head of Statistics in the School of Mathematics and Statistics at UNSW.

Yanan Fan is a Senior Lecturer at the School of Mathematics and Statistics at UNSW.

Mark Beaumont is Professor of Statistics at the University of Bristol.

Inhaltsverzeichnis

Introduction

Overview of approximate Bayesian computation: S. A. Sisson, Y. Fan and M. A. Beaumont

On the history of ABC: S.Tavare

Regression approaches: M. G. B. Blum

Monte Carlo samplers for ABC: Y. Fan and S. A. Sisson

Summary statistics: D. Prangle

Likelihood-free model choose: J.-M. Marin, P. Pudlo, A. Estoup and C. Robert

ABC and indirect inference: C. C. Drovandi

High-dimensional ABC: D. Nott, V. Ong, Y. Fan and S. A. Sisson Theoretical and methodological aspects of MCMC computations with noisy likelihoods: C. Andrieu, A.Lee and M. Viola

Informed Choices: How to calibrate ABC with hypothesis testing: O. Ratmann, A. Camacho, S. Hu and C. Coljin

Approximating the likelihood in approximate Bayesian computation: C. C. Drovandi, C. Grazian, K. Mengersen and C. Robert

Software: D.Wegmann

Divide and conquer in ABC: Expectation-Propagation algorithms for likelihood-free inference: S. Barthelme, N. Chopin and V. Cottet

SMC-ABC methods for estimation of stochastic simulation models of the limit order book: G.W. Peters, E. Panayi and F. Septier

Inferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data: G. S. Rodrigues, S. A. Sisson, M. M. Tanaka

ABC in Systems Biology: J. Liepe and M. P. H. Stumpf

Application of approximate Bayesian computation to make inference about the genetic history of Pygmy hunter-gatherers populations from Western Central Africa: A. Estoup et al

ABC for climate: dealing with expensive simulators: P. B. Holden, N. R. Edwards, J. Hensman and R. D. Wilkinson

ABC in ecological modelling: M. Fasiolo and S. N. Wood

ABC in Nuclear Imaging: Y. Fan, S. R. Meikle, G. Angelis and A. Sitek

Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 680
ISBN-13: 9780367733728
ISBN-10: 0367733722
Einband: Kartoniert / Broschiert
Redaktion: Beaumont, Mark
Sisson, Scott A.
Fan, Yanan
Hersteller: Taylor & Francis Ltd
Maße: 156 x 234 x 42 mm
Von/Mit: Mark Beaumont (u. a.)
Erscheinungsdatum: 18.12.2020
Gewicht: 1,04 kg
preigu-id: 127165626
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