- Title Information
- Title
- Bayesian Centroid Estimation
- Name:
Personal
- Name Part
- Carvalho, Luis E. X.
- Role
- Role Term:
Text
- creator
- Origin Information
- Copyright Date
(keyDate="yes", encoding="w3cdtf")
- 2008
- Physical Description
- Extent
- xv, 87 p.
- digitalOrigin
- born digital
- Note
- Thesis (Ph.D.) -- Brown University (2009)
- Name:
Personal
- Name Part
- Lawrence, Charles
- Role
- Role Term:
Text
- director
- Name:
Personal
- Name Part
- Geman, Stuart
- Role
- Role Term:
Text
- reader
- Name:
Personal
- Name Part
- Raphael, Ben
- Role
- Role Term:
Text
- reader
- Name:
Personal
- Name Part
- Brodsky, Alexander
- Role
- Role Term:
Text
- reader
- Name:
Corporate
- Name Part
- Brown University. Applied Mathematics
- Role
- Role Term:
Text
- sponsor
- Genre (aat)
- theses
- Abstract
- Maximum likelihood estimators have traditionally dominated discrete inference for a long time. In this work we apply statistical decision theory to derive a new contender that
minimizes posterior Hamming loss: the centroid estimator. We show that the centroid estimator is also the minimum Bayes risk estimator for a family of loss functions, including the important
case when the data being compared is not only categorical, but also ordinal and interval. The centroid estimator is formally characterized as a solution to a discrete optimization problem having
posterior marginal distributions as inputs. We discuss both specific constraints of interest and broad conditions under which this optimization problem becomes tractable, present efficient
algorithms for its solution, and offer further generalizations to centroid estimation. We apply centroid estimation to many applications of general, well-known models---partition set pertinence
models, hidden Markov models, change point models, and graphical models---and to classical problems in computational biology---sequence alignment, RNA secondary structure prediction, and
reconstruction of ancestral states.
- Subject (Local)
- Topic
- centroid estimator
- Subject (Local)
- Topic
- high-dimensional discrete space
- Subject (Local)
- Topic
- statistical inference
- Subject (FAST)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1012127")
- Topic
- Mathematical statistics
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20091218
- Language
- Language Term:
Code (ISO639-2B)
- eng
- Language Term:
Text
- English
- Identifier:
DOI
- 10.7301/Z00G3HHZ
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations