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20241006213017.0 |
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211022s2021 sz a ob 001 0 eng d |
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|a 1277278646
|a 1280046152
|a 1280105334
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|a 9783030828080
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|a 10.1007/978-3-030-82808-0
|2 doi
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|a HCDD
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| 100 |
1 |
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|a Heard, Nicholas,
|e author.
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| 245 |
1 |
3 |
|a An introduction to Bayesian inference, methods and computation /
|c Nick Heard.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c 2021.
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| 300 |
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|a 1 online resource (xii, 169 pages) :
|b illustrations (some color)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
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| 347 |
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|b PDF
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| 505 |
0 |
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|a Uncertainty and Decisions -- Prior and Likelihood Representation -- Graphical Modeling -- Parametric Models -- Computational Inference -- Bayesian Software Packages -- Model choice -- Linear Models -- Nonparametric Models -- Nonparametric Regression -- Clustering and Latent Factor Models -- Conjugate Parametric Models.
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| 504 |
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|a Includes bibliographical references and index.
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| 520 |
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|a 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.
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| 588 |
0 |
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|a Online resource; title from PDF title page (SpringerLink, viewed October 22, 2021).
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| 650 |
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0 |
|a Bayesian statistical decision theory.
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| 650 |
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7 |
|a Estadística bayesiana
|2 embne
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| 650 |
|
7 |
|a Bayesian statistical decision theory
|2 fast
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| 758 |
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|i has work:
|a An introduction to Bayesian inference, methods and computation (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCGYJGKPdK9Jd8RYRWXvv9C
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 776 |
0 |
8 |
|i Print version:
|a Heard, Nicholas.
|t Introduction to Bayesian inference, methods and computation.
|d Cham, Switzerland : Springer, 2021
|z 3030828077
|z 9783030828073
|w (OCoLC)1259046207
|
| 856 |
4 |
0 |
|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-030-82808-0
|y Click for online access
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| 903 |
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|a SPRING-MATH2021
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| 994 |
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|a 92
|b HCD
|