Bayesian Inference in Wavelet-Based Models:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
1999
|
| Schriftenreihe: | Lecture Notes in Statistics
141 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support |
| Beschreibung: | 1 Online-Ressource (XIV, 394p) |
| ISBN: | 9781461205678 9780387988856 |
| ISSN: | 0930-0325 |
| DOI: | 10.1007/978-1-4612-0567-8 |
Internformat
MARC
| LEADER | 00000nmm a2200000zcb4500 | ||
|---|---|---|---|
| 001 | BV042419557 | ||
| 003 | DE-604 | ||
| 005 | 00000000000000.0 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 150317s1999 |||| o||u| ||||||eng d | ||
| 020 | |a 9781461205678 |c Online |9 978-1-4612-0567-8 | ||
| 020 | |a 9780387988856 |c Print |9 978-0-387-98885-6 | ||
| 024 | 7 | |a 10.1007/978-1-4612-0567-8 |2 doi | |
| 035 | |a (OCoLC)863743199 | ||
| 035 | |a (DE-599)BVBBV042419557 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-384 |a DE-703 |a DE-91 |a DE-634 | ||
| 082 | 0 | |a 519.5 |2 23 | |
| 084 | |a MAT 000 |2 stub | ||
| 100 | 1 | |a Müller, Peter |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Bayesian Inference in Wavelet-Based Models |c edited by Peter Müller, Brani Vidakovic |
| 264 | 1 | |a New York, NY |b Springer New York |c 1999 | |
| 300 | |a 1 Online-Ressource (XIV, 394p) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 490 | 0 | |a Lecture Notes in Statistics |v 141 |x 0930-0325 | |
| 500 | |a This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support | ||
| 650 | 4 | |a Statistics | |
| 650 | 4 | |a Statistics, general | |
| 650 | 4 | |a Statistik | |
| 650 | 0 | 7 | |a Wavelet |0 (DE-588)4215427-3 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Wellenpaket |0 (DE-588)4127186-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Regressionsmodell |0 (DE-588)4127980-3 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Regressionsmodell |0 (DE-588)4127980-3 |D s |
| 689 | 0 | 1 | |a Wavelet |0 (DE-588)4215427-3 |D s |
| 689 | 0 | 2 | |a Bayes-Verfahren |0 (DE-588)4204326-8 |D s |
| 689 | 0 | |8 1\p |5 DE-604 | |
| 689 | 1 | 0 | |a Wellenpaket |0 (DE-588)4127186-5 |D s |
| 689 | 1 | |8 2\p |5 DE-604 | |
| 700 | 1 | |a Vidakovic, Brani |e Sonstige |4 oth | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-1-4612-0567-8 |x Verlag |3 Volltext |
| 912 | |a ZDB-2-SMA |a ZDB-2-BAE | ||
| 940 | 1 | |q ZDB-2-SMA_Archive | |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-027854974 | ||
| 883 | 1 | |8 1\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
| 883 | 1 | |8 2\p |a cgwrk |d 20201028 |q DE-101 |u https://d-nb.info/provenance/plan#cgwrk | |
Datensatz im Suchindex
| _version_ | 1804153090352349184 |
|---|---|
| any_adam_object | |
| author | Müller, Peter |
| author_facet | Müller, Peter |
| author_role | aut |
| author_sort | Müller, Peter |
| author_variant | p m pm |
| building | Verbundindex |
| bvnumber | BV042419557 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)863743199 (DE-599)BVBBV042419557 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-0567-8 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03300nmm a2200553zcb4500</leader><controlfield tag="001">BV042419557</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">00000000000000.0</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">150317s1999 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781461205678</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-4612-0567-8</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780387988856</subfield><subfield code="c">Print</subfield><subfield code="9">978-0-387-98885-6</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/978-1-4612-0567-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)863743199</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV042419557</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-384</subfield><subfield code="a">DE-703</subfield><subfield code="a">DE-91</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">MAT 000</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Müller, Peter</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Inference in Wavelet-Based Models</subfield><subfield code="c">edited by Peter Müller, Brani Vidakovic</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York, NY</subfield><subfield code="b">Springer New York</subfield><subfield code="c">1999</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (XIV, 394p)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Lecture Notes in Statistics</subfield><subfield code="v">141</subfield><subfield code="x">0930-0325</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistics, general</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Statistik</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Wavelet</subfield><subfield code="0">(DE-588)4215427-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Wellenpaket</subfield><subfield code="0">(DE-588)4127186-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Bayes-Verfahren</subfield><subfield code="0">(DE-588)4204326-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Regressionsmodell</subfield><subfield code="0">(DE-588)4127980-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Regressionsmodell</subfield><subfield code="0">(DE-588)4127980-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Wavelet</subfield><subfield code="0">(DE-588)4215427-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Bayes-Verfahren</subfield><subfield code="0">(DE-588)4204326-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="8">1\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Wellenpaket</subfield><subfield code="0">(DE-588)4127186-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Vidakovic, Brani</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/978-1-4612-0567-8</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-2-SMA</subfield><subfield code="a">ZDB-2-BAE</subfield></datafield><datafield tag="940" ind1="1" ind2=" "><subfield code="q">ZDB-2-SMA_Archive</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027854974</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">1\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield><datafield tag="883" ind1="1" ind2=" "><subfield code="8">2\p</subfield><subfield code="a">cgwrk</subfield><subfield code="d">20201028</subfield><subfield code="q">DE-101</subfield><subfield code="u">https://d-nb.info/provenance/plan#cgwrk</subfield></datafield></record></collection> |
| id | DE-604.BV042419557 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:05Z |
| institution | BVB |
| isbn | 9781461205678 9780387988856 |
| issn | 0930-0325 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027854974 |
| oclc_num | 863743199 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (XIV, 394p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1999 |
| publishDateSearch | 1999 |
| publishDateSort | 1999 |
| publisher | Springer New York |
| record_format | marc |
| series2 | Lecture Notes in Statistics |
| spelling | Müller, Peter Verfasser aut Bayesian Inference in Wavelet-Based Models edited by Peter Müller, Brani Vidakovic New York, NY Springer New York 1999 1 Online-Ressource (XIV, 394p) txt rdacontent c rdamedia cr rdacarrier Lecture Notes in Statistics 141 0930-0325 This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of such prior models. In Part IV, some aspects of prior modeling using priors that account for dependence are explored. Part V considers the use of 2-dimensional wavelet decomposition in spatial modeling. Chapters in Part VI discuss the use of empirical Bayes estimation in wavelet based models. Part VII concludes the volume with a discussion of case studies using wavelet based Bayesian approaches. The cooperation of all contributors in the timely preparation of their manuscripts is greatly recognized. We decided early on that it was impor tant to referee and critically evaluate the papers which were submitted for inclusion in this volume. For this substantial task, we relied on the service of numerous referees to whom we are most indebted. We are also grateful to John Kimmel and the Springer-Verlag referees for considering our proposal in a very timely manner. Our special thanks go to our spouses, Gautami and Draga, for their support Statistics Statistics, general Statistik Wavelet (DE-588)4215427-3 gnd rswk-swf Wellenpaket (DE-588)4127186-5 gnd rswk-swf Bayes-Verfahren (DE-588)4204326-8 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 gnd rswk-swf Regressionsmodell (DE-588)4127980-3 s Wavelet (DE-588)4215427-3 s Bayes-Verfahren (DE-588)4204326-8 s 1\p DE-604 Wellenpaket (DE-588)4127186-5 s 2\p DE-604 Vidakovic, Brani Sonstige oth https://doi.org/10.1007/978-1-4612-0567-8 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Müller, Peter Bayesian Inference in Wavelet-Based Models Statistics Statistics, general Statistik Wavelet (DE-588)4215427-3 gnd Wellenpaket (DE-588)4127186-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Regressionsmodell (DE-588)4127980-3 gnd |
| subject_GND | (DE-588)4215427-3 (DE-588)4127186-5 (DE-588)4204326-8 (DE-588)4127980-3 |
| title | Bayesian Inference in Wavelet-Based Models |
| title_auth | Bayesian Inference in Wavelet-Based Models |
| title_exact_search | Bayesian Inference in Wavelet-Based Models |
| title_full | Bayesian Inference in Wavelet-Based Models edited by Peter Müller, Brani Vidakovic |
| title_fullStr | Bayesian Inference in Wavelet-Based Models edited by Peter Müller, Brani Vidakovic |
| title_full_unstemmed | Bayesian Inference in Wavelet-Based Models edited by Peter Müller, Brani Vidakovic |
| title_short | Bayesian Inference in Wavelet-Based Models |
| title_sort | bayesian inference in wavelet based models |
| topic | Statistics Statistics, general Statistik Wavelet (DE-588)4215427-3 gnd Wellenpaket (DE-588)4127186-5 gnd Bayes-Verfahren (DE-588)4204326-8 gnd Regressionsmodell (DE-588)4127980-3 gnd |
| topic_facet | Statistics Statistics, general Statistik Wavelet Wellenpaket Bayes-Verfahren Regressionsmodell |
| url | https://doi.org/10.1007/978-1-4612-0567-8 |
| work_keys_str_mv | AT mullerpeter bayesianinferenceinwaveletbasedmodels AT vidakovicbrani bayesianinferenceinwaveletbasedmodels |