Practical Bayesian inference: a primer for physical scientists
"Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom
Cambridge University Press
2017
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes from complexity: data may be numerous, high dimensional, and correlated, making it difficult to see structures. This book is an introduction to statistical methods for analysing data. It presents the major concepts of probability and statistics as well as the computational tools we need to extract meaning from data in the presence of uncertainty"... |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | ix, 295 Seiten Illustrationen, Diagramme |
| ISBN: | 9781107192119 9781316642214 |
Internformat
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| 650 | 4 | |a Mathematische Physik | |
| 650 | 4 | |a Bayesian statistical decision theory | |
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Datensatz im Suchindex
| _version_ | 1804177819505262592 |
|---|---|
| adam_text | PRACTICAL BAYESIAN INFERENCE
/ BAILER-JONES, CORYN A. L.YYEAUTHOR
: 2017
TABLE OF CONTENTS / INHALTSVERZEICHNIS
PROBABILITY BASICS
ESTIMATION AND UNCERTAINTY
STATISTICAL MODELS AND INFERENCE
LINEAR MODELS, LEAST SQUARES, AND MAXIMUM LIKELIHOOD
PARAMETER ESTIMATION: SINGLE PARAMETER
PARAMETER ESTIMATION: MULTIPLE PARAMETERS
APPROXIMATING DISTRIBUTIONS
MONTE CARLO METHODS FOR INFERENCE
PARAMETER ESTIMATION: MARKOV CHAIN MONTE CARLO
FREQUENTIST HYPOTHESIS TESTING
MODEL COMPARISON
DEALING WITH MORE COMPLICATED PROBLEMS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
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| callnumber-subject | QC - Physics |
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| ctrlnum | (OCoLC)1005928243 (DE-599)BVBBV044481308 |
| dewey-full | 519.5/42 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/42 |
| dewey-search | 519.5/42 |
| dewey-sort | 3519.5 242 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Book |
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| institution | BVB |
| isbn | 9781107192119 9781316642214 |
| language | English |
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| spelling | Bailer-Jones, Coryn A. L. Verfasser (DE-588)1126122696 aut Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones, Max-Planck-Institute for Astronomy, Heidelberg Cambridge, United Kingdom Cambridge University Press 2017 © 2017 ix, 295 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index "Science is fundamentally about learning from data, and doing so in the presence of uncertainty. Uncertainty arises inevitably and avoidably in many guises. It comes from noise in our measurements: we cannot measure exactly. It comes from sampling effects: we cannot measure everything. It comes from complexity: data may be numerous, high dimensional, and correlated, making it difficult to see structures. This book is an introduction to statistical methods for analysing data. It presents the major concepts of probability and statistics as well as the computational tools we need to extract meaning from data in the presence of uncertainty"... Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd rswk-swf Bayes-Inferenz (DE-588)4648118-7 s 1\p DE-604 LoC Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029881499&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bailer-Jones, Coryn A. L. Practical Bayesian inference a primer for physical scientists Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd |
| subject_GND | (DE-588)4648118-7 |
| title | Practical Bayesian inference a primer for physical scientists |
| title_auth | Practical Bayesian inference a primer for physical scientists |
| title_exact_search | Practical Bayesian inference a primer for physical scientists |
| title_full | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones, Max-Planck-Institute for Astronomy, Heidelberg |
| title_fullStr | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones, Max-Planck-Institute for Astronomy, Heidelberg |
| title_full_unstemmed | Practical Bayesian inference a primer for physical scientists Coryn A.L. Bailer-Jones, Max-Planck-Institute for Astronomy, Heidelberg |
| title_short | Practical Bayesian inference |
| title_sort | practical bayesian inference a primer for physical scientists |
| title_sub | a primer for physical scientists |
| topic | Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz (DE-588)4648118-7 gnd |
| topic_facet | Mathematische Physik Bayesian statistical decision theory Mathematical physics Bayes-Inferenz |
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