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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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