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Searching for Structure in Point Data

Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray

in Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Published in print:
2014
Published Online:
October 2017
ISBN:
9780691151687
eISBN:
9781400848911
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691151687.003.0006
Subject:
Physics, Particle Physics / Astrophysics / Cosmology

Inferring the probability density function (pdf) from a sample of data is known as density estimation. The same methodology is often called data smoothing. Density estimation in the one-dimensional ... More


13 Basic nonparametric estimates

Timo Teräsvirta, Dag Tjøstheim, and W. J. Granger

in Modelling Nonlinear Economic Time Series

Published in print:
2010
Published Online:
May 2011
ISBN:
9780199587148
eISBN:
9780191595387
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199587148.003.0013
Subject:
Economics and Finance, Econometrics

There are several books on the topics treated in this chapter. For completeness and ease of reference, in the present chapter a brief summary of some results in this area is presented. Among other ... More


Importance Estimation

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0004
Subject:
Computer Science, Machine Learning

This chapter discusses the problem of importance estimation. Importance-weighting techniques play essential roles in covariate shift adaptation. However, the importance values are usually unknown a ... More


Direct Density-Ratio Estimation with Dimensionality Reduction

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0005
Subject:
Computer Science, Machine Learning

This chapter discusses a dimensionality reduction scheme for density-ratio estimation, called direct density-ratio estimation with dimensionality reduction (D3; pronounced as “D-cube”). The basic ... More


Categorization as nonparametric Bayesian density estimation

Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, and Daniel J. Navarro

in The Probabilistic Mind:: Prospects for Bayesian cognitive science

Published in print:
2008
Published Online:
March 2012
ISBN:
9780199216093
eISBN:
9780191695971
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199216093.003.0014
Subject:
Psychology, Cognitive Psychology

The authors apply the state of the art techniques from machine learning and statistics to reconceptualize the problem of unsupervised category learning, and to relate it to previous psychologically ... More


Local Regression and Likelihood

Partha P. Mitra and Hemant Bokil

in Observed Brain Dynamics

Published in print:
2007
Published Online:
May 2009
ISBN:
9780195178081
eISBN:
9780199864829
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195178081.003.0013
Subject:
Neuroscience, Techniques, Molecular and Cellular Systems

Local regression and likelihood methods are nonparametric approaches for fitting regression functions and probability distributions to data. This chapter discusses the basic ideas behind these ... More


Dimensionality and Its Reduction

Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray

in Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Published in print:
2014
Published Online:
October 2017
ISBN:
9780691151687
eISBN:
9781400848911
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691151687.003.0007
Subject:
Physics, Particle Physics / Astrophysics / Cosmology

With the dramatic increase in data available from a new generation of astronomical telescopes and instruments, many analyses must address the question of the complexity as well as size of the data ... More


Conclusions and Future Prospects

Masashi Sugiyama and Motoaki Kawanabe

in Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation

Published in print:
2012
Published Online:
September 2013
ISBN:
9780262017091
eISBN:
9780262301220
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262017091.003.0011
Subject:
Computer Science, Machine Learning

This chapter summarizes the main themes covered in the preceding discussions and discusses future prospects. This book has provided a comprehensive overview of theory, algorithms, and applications of ... More


Statistical modelling and inference

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/9780198714934.003.0004
Subject:
Mathematics, Logic / Computer Science / Mathematical Philosophy, Mathematical Physics

The modern view of statistical machine learning and signal processing is that the central task is one of finding good probabilistic models for the joint distribution over all the variables in the ... More


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