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Statistical Alternatives and Supplements to Random Sampling

Patrick Dattalo

in Strategies to Approximate Random Sampling and Assignment

Published in print:
2009
Published Online:
February 2010
ISBN:
9780195378351
eISBN:
9780199864645
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195378351.003.0003
Subject:
Social Work, Research and Evaluation

This chapter describes the following alternatives and complements to RS in terms of their assumptions, implementations, strengths, and weaknesses: (1) randomization tests; (2) multiple imputation; ... More


Statistical machine learning

Max A. Little

in Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics

Published in print:
2019
Published Online:
October 2019
ISBN:
9780198714934
eISBN:
9780191879180
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198714934.003.0006
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy, Mathematical Physics

This chapter describes in detail how the main techniques of statistical machine learning can be constructed from the components described in earlier chapters. It presents these concepts in a way ... More


Building Generative Models: Structural Learning and Identification of the Learner

Reza Shadmehr and Sandro Mussa-Ivaldi

in Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions

Published in print:
2012
Published Online:
August 2013
ISBN:
9780262016964
eISBN:
9780262301282
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262016964.003.0010
Subject:
Neuroscience, Research and Theory

This chapter presents a discussion on structural learning and identification of the structure of the learner. It reveals that the prior exposure to a rotation perturbation, despite being random and ... More


Generative models

Thomas P. Trappenberg

in Fundamentals of Machine Learning

Published in print:
2019
Published Online:
January 2020
ISBN:
9780198828044
eISBN:
9780191883873
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198828044.003.0008
Subject:
Neuroscience, Behavioral Neuroscience

This chapter presents an introduction to the important topic of building generative models. These are models that are aimed to understand the variety of a class such as cars or trees. A generative ... More


Network statistics and measurement error

Mark Newman

in Networks

Published in print:
2018
Published Online:
October 2018
ISBN:
9780198805090
eISBN:
9780191843235
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780198805090.003.0009
Subject:
Physics, Theoretical, Computational, and Statistical Physics

This chapter introduces the mathematics of network statistics, the quantification of errors in network data, and the estimation of network structure in the presence of error. The discussion starts ... More


Entropy Regularization

Grandvalet Yves and Bengio Yoshua

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0009
Subject:
Computer Science, Machine Learning

This chapter promotes the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly ... More


Empirical and semi-empirical models of codon evolution

Adrian Schneider and Gina M. Cannarozzi

in Codon Evolution: Mechanisms and Models

Published in print:
2012
Published Online:
May 2015
ISBN:
9780199601165
eISBN:
9780191810114
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:osobl/9780199601165.003.0003
Subject:
Biology, Evolutionary Biology / Genetics

This chapter first describes the empirical codon model presented by Schneider et al. in 2005, demonstrating its advantages over amino acid models for aligning coding sequences. It then outlines two ... More


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