Bayesian field theory /:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Baltimore, Md. :
Johns Hopkins University Press,
2003.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 1 online resource (xix, 411 pages) : illustrations |
| Bibliographie: | Includes bibliographical references (pages 365-402) and index. |
| ISBN: | 0801877970 9780801877971 |
Internformat
MARC
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| 100 | 1 | |a Lemm, Jörg C. | |
| 245 | 1 | 0 | |a Bayesian field theory / |c Jörg C. Lemm. |
| 260 | |a Baltimore, Md. : |b Johns Hopkins University Press, |c 2003. | ||
| 300 | |a 1 online resource (xix, 411 pages) : |b illustrations | ||
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| 504 | |a Includes bibliographical references (pages 365-402) and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Contents; List of Figures; List of Tables; List of Numerical Case Studies; Acknowledgments; 1 Introduction; 2 Bayesian framework; 2.1 Bayesian models; 2.1.1 Independent, dependent, and hidden variables; 2.1.2 Energies, free energies, and errors; 2.1.3 Bayes' theorem: Posterior, prior, and likelihood; 2.1.4 Predictive density and learning; 2.1.5 Mutual information and learning; 2.1.6 Maximum A Posteriori Approximation (MAP); 2.1.7 Normalization, non-negativity, and specific priors; 2.1.8 Numerical case study: A fair coin?; 2.2 Bayesian decision theory; 2.2.1 Loss and risk. | |
| 505 | 8 | |a 2.2.2 Loss functions for approximation2.2.3 General loss functions and unsupervised learning; 2.2.4 Log-loss and Maximum A Posteriori Approximation; 2.2.5 Empirical risk minimization; 2.2.6 Interpretations of Occam's razor; 2.2.7 Approaches to empirical learning; 2.3 A priori information; 2.3.1 Controlled, measured, and structural priors; 2.3.2 Noise induced priors; 3 Gaussian prior factors; 3.1 Gaussian prior factor for log-likelihoods; 3.1.1 Lagrange multipliers: Error functional E(L); 3.1.2 Normalization by parameterization: Error functional E(g); 3.1.3 The Hessians H[sub(L)], H[sub(g)]. | |
| 505 | 8 | |a 3.2 Gaussian prior factor for likelihoods3.2.1 Lagrange multipliers: Error functional E(P); 3.2.2 Normalization by parameterization: Error functional E(z); 3.2.3 The Hessians H[sub(P)], H[sub(z)]; 3.3 Quadratic density estimation and empirical risk minimization; 3.4 Numerical case study: Density estimation with Gaussian specific priors; 3.5 Gaussian prior factors for general field; 3.5.1 The general case; 3.5.2 Square root of P; 3.5.3 Distribution functions; 3.6 Covariances and invariances; 3.6.1 Approximate invariance; 3.6.2 Infinitesimal translations; 3.6.3 Approximate periodicity. | |
| 505 | 8 | |a 3.6.4 Approximate fractals3.7 Non-zero means; 3.8 Regression; 3.8.1 Gaussian regression; 3.8.2 Exact predictive density; 3.8.3 Gaussian mixture regression (cluster regression); 3.8.4 Support vector machines and regression; 3.8.5 Numerical case study: Approximately invariant regression (AIR); 3.9 Classification; 4 Parameterizing likelihoods: Variational methods; 4.1 General likelihood parameterizations; 4.2 Gaussian priors for likelihood parameters; 4.3 Linear trial spaces; 4.4 Linear regression; 4.5 Mixture models; 4.6 Additive models; 4.7 Product ansatz; 4.8 Decision trees. | |
| 505 | 8 | |a 4.9 Projection pursuit4.10 Neural networks; 5 Parameterizing priors: Hyperparameters; 5.1 Quenched and annealed prior normalization; 5.2 Saddle point approximations and hyperparameters; 5.2.1 Joint MAP; 5.2.2 Stepwise MAP; 5.2.3 Pointwise approximation; 5.2.4 Marginal posterior and empirical Bayes; 5.2.5 Some variants of stationarity equations; 5.3 Adapting prior, means; 5.3.1 General considerations; 5.3.2 Density estimation and nonparametric boosting; 5.3.3 Unrestricted variation; 5.3.4 Regression; 5.4 Adapting prior covariances; 5.4.1 General case; 5.4.2 Automatic relevance detection. | |
| 650 | 0 | |a Bayesian field theory. |0 http://id.loc.gov/authorities/subjects/sh2002004440 | |
| 650 | 6 | |a Théorie des champs bayésienne. | |
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Datensatz im Suchindex
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| _version_ | 1816881609498951680 |
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| author | Lemm, Jörg C. |
| author_facet | Lemm, Jörg C. |
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| callnumber-sort | QC 3174.85 B38 L45 42003EB |
| callnumber-subject | QC - Physics |
| collection | ZDB-4-EBA |
| contents | Cover; Contents; List of Figures; List of Tables; List of Numerical Case Studies; Acknowledgments; 1 Introduction; 2 Bayesian framework; 2.1 Bayesian models; 2.1.1 Independent, dependent, and hidden variables; 2.1.2 Energies, free energies, and errors; 2.1.3 Bayes' theorem: Posterior, prior, and likelihood; 2.1.4 Predictive density and learning; 2.1.5 Mutual information and learning; 2.1.6 Maximum A Posteriori Approximation (MAP); 2.1.7 Normalization, non-negativity, and specific priors; 2.1.8 Numerical case study: A fair coin?; 2.2 Bayesian decision theory; 2.2.1 Loss and risk. 2.2.2 Loss functions for approximation2.2.3 General loss functions and unsupervised learning; 2.2.4 Log-loss and Maximum A Posteriori Approximation; 2.2.5 Empirical risk minimization; 2.2.6 Interpretations of Occam's razor; 2.2.7 Approaches to empirical learning; 2.3 A priori information; 2.3.1 Controlled, measured, and structural priors; 2.3.2 Noise induced priors; 3 Gaussian prior factors; 3.1 Gaussian prior factor for log-likelihoods; 3.1.1 Lagrange multipliers: Error functional E(L); 3.1.2 Normalization by parameterization: Error functional E(g); 3.1.3 The Hessians H[sub(L)], H[sub(g)]. 3.2 Gaussian prior factor for likelihoods3.2.1 Lagrange multipliers: Error functional E(P); 3.2.2 Normalization by parameterization: Error functional E(z); 3.2.3 The Hessians H[sub(P)], H[sub(z)]; 3.3 Quadratic density estimation and empirical risk minimization; 3.4 Numerical case study: Density estimation with Gaussian specific priors; 3.5 Gaussian prior factors for general field; 3.5.1 The general case; 3.5.2 Square root of P; 3.5.3 Distribution functions; 3.6 Covariances and invariances; 3.6.1 Approximate invariance; 3.6.2 Infinitesimal translations; 3.6.3 Approximate periodicity. 3.6.4 Approximate fractals3.7 Non-zero means; 3.8 Regression; 3.8.1 Gaussian regression; 3.8.2 Exact predictive density; 3.8.3 Gaussian mixture regression (cluster regression); 3.8.4 Support vector machines and regression; 3.8.5 Numerical case study: Approximately invariant regression (AIR); 3.9 Classification; 4 Parameterizing likelihoods: Variational methods; 4.1 General likelihood parameterizations; 4.2 Gaussian priors for likelihood parameters; 4.3 Linear trial spaces; 4.4 Linear regression; 4.5 Mixture models; 4.6 Additive models; 4.7 Product ansatz; 4.8 Decision trees. 4.9 Projection pursuit4.10 Neural networks; 5 Parameterizing priors: Hyperparameters; 5.1 Quenched and annealed prior normalization; 5.2 Saddle point approximations and hyperparameters; 5.2.1 Joint MAP; 5.2.2 Stepwise MAP; 5.2.3 Pointwise approximation; 5.2.4 Marginal posterior and empirical Bayes; 5.2.5 Some variants of stationarity equations; 5.3 Adapting prior, means; 5.3.1 General considerations; 5.3.2 Density estimation and nonparametric boosting; 5.3.3 Unrestricted variation; 5.3.4 Regression; 5.4 Adapting prior covariances; 5.4.1 General case; 5.4.2 Automatic relevance detection. |
| ctrlnum | (OCoLC)52762436 |
| dewey-full | 530.15/95 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 530 - Physics |
| dewey-raw | 530.15/95 |
| dewey-search | 530.15/95 |
| dewey-sort | 3530.15 295 |
| dewey-tens | 530 - Physics |
| discipline | Physik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-ocm52762436 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:15:26Z |
| institution | BVB |
| isbn | 0801877970 9780801877971 |
| language | English |
| oclc_num | 52762436 |
| open_access_boolean | |
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| publisher | Johns Hopkins University Press, |
| record_format | marc |
| spelling | Lemm, Jörg C. Bayesian field theory / Jörg C. Lemm. Baltimore, Md. : Johns Hopkins University Press, 2003. 1 online resource (xix, 411 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references (pages 365-402) and index. Print version record. Cover; Contents; List of Figures; List of Tables; List of Numerical Case Studies; Acknowledgments; 1 Introduction; 2 Bayesian framework; 2.1 Bayesian models; 2.1.1 Independent, dependent, and hidden variables; 2.1.2 Energies, free energies, and errors; 2.1.3 Bayes' theorem: Posterior, prior, and likelihood; 2.1.4 Predictive density and learning; 2.1.5 Mutual information and learning; 2.1.6 Maximum A Posteriori Approximation (MAP); 2.1.7 Normalization, non-negativity, and specific priors; 2.1.8 Numerical case study: A fair coin?; 2.2 Bayesian decision theory; 2.2.1 Loss and risk. 2.2.2 Loss functions for approximation2.2.3 General loss functions and unsupervised learning; 2.2.4 Log-loss and Maximum A Posteriori Approximation; 2.2.5 Empirical risk minimization; 2.2.6 Interpretations of Occam's razor; 2.2.7 Approaches to empirical learning; 2.3 A priori information; 2.3.1 Controlled, measured, and structural priors; 2.3.2 Noise induced priors; 3 Gaussian prior factors; 3.1 Gaussian prior factor for log-likelihoods; 3.1.1 Lagrange multipliers: Error functional E(L); 3.1.2 Normalization by parameterization: Error functional E(g); 3.1.3 The Hessians H[sub(L)], H[sub(g)]. 3.2 Gaussian prior factor for likelihoods3.2.1 Lagrange multipliers: Error functional E(P); 3.2.2 Normalization by parameterization: Error functional E(z); 3.2.3 The Hessians H[sub(P)], H[sub(z)]; 3.3 Quadratic density estimation and empirical risk minimization; 3.4 Numerical case study: Density estimation with Gaussian specific priors; 3.5 Gaussian prior factors for general field; 3.5.1 The general case; 3.5.2 Square root of P; 3.5.3 Distribution functions; 3.6 Covariances and invariances; 3.6.1 Approximate invariance; 3.6.2 Infinitesimal translations; 3.6.3 Approximate periodicity. 3.6.4 Approximate fractals3.7 Non-zero means; 3.8 Regression; 3.8.1 Gaussian regression; 3.8.2 Exact predictive density; 3.8.3 Gaussian mixture regression (cluster regression); 3.8.4 Support vector machines and regression; 3.8.5 Numerical case study: Approximately invariant regression (AIR); 3.9 Classification; 4 Parameterizing likelihoods: Variational methods; 4.1 General likelihood parameterizations; 4.2 Gaussian priors for likelihood parameters; 4.3 Linear trial spaces; 4.4 Linear regression; 4.5 Mixture models; 4.6 Additive models; 4.7 Product ansatz; 4.8 Decision trees. 4.9 Projection pursuit4.10 Neural networks; 5 Parameterizing priors: Hyperparameters; 5.1 Quenched and annealed prior normalization; 5.2 Saddle point approximations and hyperparameters; 5.2.1 Joint MAP; 5.2.2 Stepwise MAP; 5.2.3 Pointwise approximation; 5.2.4 Marginal posterior and empirical Bayes; 5.2.5 Some variants of stationarity equations; 5.3 Adapting prior, means; 5.3.1 General considerations; 5.3.2 Density estimation and nonparametric boosting; 5.3.3 Unrestricted variation; 5.3.4 Regression; 5.4 Adapting prior covariances; 5.4.1 General case; 5.4.2 Automatic relevance detection. Bayesian field theory. http://id.loc.gov/authorities/subjects/sh2002004440 Théorie des champs bayésienne. SCIENCE Physics Mathematical & Computational. bisacsh Bayesian field theory fast has work: Bayesian field theory (Text) https://id.oclc.org/worldcat/entity/E39PCXYXG3K6kFYC3mkBfHMmGw https://id.oclc.org/worldcat/ontology/hasWork Print version: Lemm, Jörg C. Bayesian field theory. Baltimore, Md. : Johns Hopkins University Press, 2003 0801872200 (DLC) 2002073958 (OCoLC)50184931 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=79367 Volltext |
| spellingShingle | Lemm, Jörg C. Bayesian field theory / Cover; Contents; List of Figures; List of Tables; List of Numerical Case Studies; Acknowledgments; 1 Introduction; 2 Bayesian framework; 2.1 Bayesian models; 2.1.1 Independent, dependent, and hidden variables; 2.1.2 Energies, free energies, and errors; 2.1.3 Bayes' theorem: Posterior, prior, and likelihood; 2.1.4 Predictive density and learning; 2.1.5 Mutual information and learning; 2.1.6 Maximum A Posteriori Approximation (MAP); 2.1.7 Normalization, non-negativity, and specific priors; 2.1.8 Numerical case study: A fair coin?; 2.2 Bayesian decision theory; 2.2.1 Loss and risk. 2.2.2 Loss functions for approximation2.2.3 General loss functions and unsupervised learning; 2.2.4 Log-loss and Maximum A Posteriori Approximation; 2.2.5 Empirical risk minimization; 2.2.6 Interpretations of Occam's razor; 2.2.7 Approaches to empirical learning; 2.3 A priori information; 2.3.1 Controlled, measured, and structural priors; 2.3.2 Noise induced priors; 3 Gaussian prior factors; 3.1 Gaussian prior factor for log-likelihoods; 3.1.1 Lagrange multipliers: Error functional E(L); 3.1.2 Normalization by parameterization: Error functional E(g); 3.1.3 The Hessians H[sub(L)], H[sub(g)]. 3.2 Gaussian prior factor for likelihoods3.2.1 Lagrange multipliers: Error functional E(P); 3.2.2 Normalization by parameterization: Error functional E(z); 3.2.3 The Hessians H[sub(P)], H[sub(z)]; 3.3 Quadratic density estimation and empirical risk minimization; 3.4 Numerical case study: Density estimation with Gaussian specific priors; 3.5 Gaussian prior factors for general field; 3.5.1 The general case; 3.5.2 Square root of P; 3.5.3 Distribution functions; 3.6 Covariances and invariances; 3.6.1 Approximate invariance; 3.6.2 Infinitesimal translations; 3.6.3 Approximate periodicity. 3.6.4 Approximate fractals3.7 Non-zero means; 3.8 Regression; 3.8.1 Gaussian regression; 3.8.2 Exact predictive density; 3.8.3 Gaussian mixture regression (cluster regression); 3.8.4 Support vector machines and regression; 3.8.5 Numerical case study: Approximately invariant regression (AIR); 3.9 Classification; 4 Parameterizing likelihoods: Variational methods; 4.1 General likelihood parameterizations; 4.2 Gaussian priors for likelihood parameters; 4.3 Linear trial spaces; 4.4 Linear regression; 4.5 Mixture models; 4.6 Additive models; 4.7 Product ansatz; 4.8 Decision trees. 4.9 Projection pursuit4.10 Neural networks; 5 Parameterizing priors: Hyperparameters; 5.1 Quenched and annealed prior normalization; 5.2 Saddle point approximations and hyperparameters; 5.2.1 Joint MAP; 5.2.2 Stepwise MAP; 5.2.3 Pointwise approximation; 5.2.4 Marginal posterior and empirical Bayes; 5.2.5 Some variants of stationarity equations; 5.3 Adapting prior, means; 5.3.1 General considerations; 5.3.2 Density estimation and nonparametric boosting; 5.3.3 Unrestricted variation; 5.3.4 Regression; 5.4 Adapting prior covariances; 5.4.1 General case; 5.4.2 Automatic relevance detection. Bayesian field theory. http://id.loc.gov/authorities/subjects/sh2002004440 Théorie des champs bayésienne. SCIENCE Physics Mathematical & Computational. bisacsh Bayesian field theory fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004440 |
| title | Bayesian field theory / |
| title_auth | Bayesian field theory / |
| title_exact_search | Bayesian field theory / |
| title_full | Bayesian field theory / Jörg C. Lemm. |
| title_fullStr | Bayesian field theory / Jörg C. Lemm. |
| title_full_unstemmed | Bayesian field theory / Jörg C. Lemm. |
| title_short | Bayesian field theory / |
| title_sort | bayesian field theory |
| topic | Bayesian field theory. http://id.loc.gov/authorities/subjects/sh2002004440 Théorie des champs bayésienne. SCIENCE Physics Mathematical & Computational. bisacsh Bayesian field theory fast |
| topic_facet | Bayesian field theory. Théorie des champs bayésienne. SCIENCE Physics Mathematical & Computational. Bayesian field theory |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=79367 |
| work_keys_str_mv | AT lemmjorgc bayesianfieldtheory |