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on1347025204 |
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OCoLC |
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20241006213017.0 |
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m o d |
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cr cnu|||||||| |
| 008 |
230209s2012 xx o ||| 0 eng d |
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|a EBLCP
|b eng
|c EBLCP
|d OCLCQ
|d OCLCO
|d OCLCQ
|d EBLCP
|d OCLCQ
|d OCLCL
|d OCLCQ
|d UEJ
|d OCLCO
|d OCLCQ
|d OCLCO
|d OCLCQ
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| 020 |
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|a 9781118359754
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| 020 |
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|a 1118359755
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| 035 |
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|a (OCoLC)1347025204
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| 050 |
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4 |
|a QA279.5
|b .L44 2012
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| 049 |
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|a HCDD
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| 100 |
1 |
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|a Lee, Peter M.
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| 245 |
1 |
0 |
|a Bayesian Statistics
|h [electronic resource] :
|b An Introduction.
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| 260 |
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2012.
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| 300 |
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|a 1 online resource (488 p.).
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| 490 |
1 |
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|a New York Academy of Sciences Ser.
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| 500 |
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|a Description based upon print version of record.
|
| 505 |
0 |
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|a Intro -- Bayesian Statistics -- Contents -- Preface -- Preface to the First Edition -- 1 Preliminaries -- 1.1 Probability and Bayes' Theorem -- 1.1.1 Notation -- 1.1.2 Axioms for probability -- 1.1.3 'Unconditional' probability -- 1.1.4 Odds -- 1.1.5 Independence -- 1.1.6 Some simple consequences of the axioms -- Bayes' Theorem -- 1.2 Examples on Bayes' Theorem -- 1.2.1 The Biology of Twins -- 1.2.2 A political example -- 1.2.3 A warning -- 1.3 Random variables -- 1.3.1 Discrete random variables -- 1.3.2 The binomial distribution -- 1.3.3 Continuous random variables
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| 505 |
8 |
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|a 1.3.4 The normal distribution -- 1.3.5 Mixed random variables -- 1.4 Several random variables -- 1.4.1 Two discrete random variables -- 1.4.2 Two continuous random variables -- 1.4.3 Bayes' Theorem for random variables -- 1.4.4 Example -- 1.4.5 One discrete variable and one continuous variable -- 1.4.6 Independent random variables -- 1.5 Means and variances -- 1.5.1 Expectations -- 1.5.2 The expectation of a sum and of a product -- 1.5.3 Variance, precision and standard deviation -- 1.5.4 Examples -- 1.5.5 Variance of a sum -- covariance and correlation
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| 505 |
8 |
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|a 1.5.6 Approximations to the mean and variance of a function of a random variable -- 1.5.7 Conditional expectations and variances -- 1.5.8 Medians and modes -- 1.6 Exercises on Chapter 1 -- 2 Bayesian inference for the normal distribution -- 2.1 Nature of Bayesian inference -- 2.1.1 Preliminary remarks -- 2.1.2 Post is prior times likelihood -- 2.1.3 Likelihood can be multiplied by any constant -- 2.1.4 Sequential use of Bayes' Theorem -- 2.1.5 The predictive distribution -- 2.1.6 A warning -- 2.2 Normal prior and likelihood -- 2.2.1 Posterior from a normal prior and likelihood -- 2.2.2 Example
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| 505 |
8 |
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|a 2.2.3 Predictive distribution -- 2.2.4 The nature of the assumptions made -- 2.3 Several normal observations with a normal prior -- 2.3.1 Posterior distribution -- 2.3.2 Example -- 2.3.3 Predictive distribution -- 2.3.4 Robustness -- 2.4 Dominant likelihoods -- 2.4.1 Improper priors -- 2.4.2 Approximation of proper priors by improper priors -- 2.5 Locally uniform priors -- 2.5.1 Bayes' postulate -- 2.5.2 Data translated likelihoods -- 2.5.3 Transformation of unknown parameters -- 2.6 Highest density regions -- 2.6.1 Need for summaries of posterior information
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| 505 |
8 |
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|a 2.6.2 Relation to classical statistics -- 2.7 Normal variance -- 2.7.1 A suitable prior for the normal variance -- 2.7.2 Reference prior for the normal variance -- 2.8 HDRs for the normal variance -- 2.8.1 What distribution should we be considering? -- 2.8.2 Example -- 2.9 The role of sufficiency -- 2.9.1 Definition of sufficiency -- 2.9.2 Neyman's factorization theorem -- 2.9.3 Sufficiency principle -- 2.9.4 Examples -- 2.9.5 Order statistics and minimal sufficient statistics -- 2.9.6 Examples on minimal sufficiency -- 2.10 Conjugate prior distributions -- 2.10.1 Definition and difficulties
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| 500 |
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|a 2.10.2 Examples
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| 650 |
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0 |
|a Bayesian statistical decision theory.
|1 http://www.wikidata.org/entity/Q812535
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| 650 |
|
0 |
|a Mathematical statistics.
|
| 650 |
|
0 |
|a Bayesian statistical decision theory.
|
| 655 |
|
0 |
|a Electronic books.
|
| 758 |
|
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|i has work:
|a Bayesian statistics (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFQfd4PfFJXFkWXkykPXv3
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Lee, Peter M.
|t Bayesian Statistics
|d Newark : John Wiley & Sons, Incorporated,c2012
|z 9781118332573
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| 830 |
|
0 |
|a New York Academy of Sciences Ser.
|
| 856 |
4 |
0 |
|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=7103580
|y Click for online access
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| 903 |
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|a EBC-AC
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| 994 |
|
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|a 92
|b HCD
|