Optimal Bayesian Classification When the Training Observations are Serially Dependent

Amin Zollanvari; Edward R. Dougherty

doi:10.1109/NYSDS.2018.8538943

Optimal Bayesian Classification When the Training Observations are Serially Dependent

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, we construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR(p), which is a multi-dimensional vector autoregressive process of order p.

Original language	English
Title of host publication	2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538679333
DOIs	https://doi.org/10.1109/NYSDS.2018.8538943
Publication status	Published - Nov 16 2018
Event	2018 New York Scientific Data Summit, NYSDS 2018 - Upton, United States Duration: Aug 6 2018 → Aug 8 2018

Publication series

Name	2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings

Conference

Conference	2018 New York Scientific Data Summit, NYSDS 2018
Country	United States
City	Upton
Period	8/6/18 → 8/8/18

Keywords

Optimal Bayesian Classification
Vector Autoregressive Processes

ASJC Scopus subject areas

Computer Graphics and Computer-Aided Design
Computer Science Applications
Signal Processing
Modelling and Simulation

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Zollanvari, A., & Dougherty, E. R. (2018). Optimal Bayesian Classification When the Training Observations are Serially Dependent. In 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings [8538943] (2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NYSDS.2018.8538943

Optimal Bayesian Classification When the Training Observations are Serially Dependent. / Zollanvari, Amin; Dougherty, Edward R.

2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8538943 (2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Zollanvari, A & Dougherty, ER 2018, Optimal Bayesian Classification When the Training Observations are Serially Dependent. in 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings., 8538943, 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 New York Scientific Data Summit, NYSDS 2018, Upton, United States, 8/6/18. https://doi.org/10.1109/NYSDS.2018.8538943

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abstract = "In this study, we construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR(p), which is a multi-dimensional vector autoregressive process of order p.",

keywords = "Optimal Bayesian Classification, Vector Autoregressive Processes",

author = "Amin Zollanvari and Dougherty, {Edward R.}",

note = "Funding Information: ACKNOWLEDGMENT This work was partially supported by Nazarbayev University Grant SOE2018008.; 2018 New York Scientific Data Summit, NYSDS 2018 ; Conference date: 06-08-2018 Through 08-08-2018",

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Optimal Bayesian Classification When the Training Observations are Serially Dependent

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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