Bayesian nonparametric modeling of categorical data for information fusion and causal inference

Sihan Xiong, Yiwei Fu, Asok Ray

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.

Original languageEnglish (US)
Article number396
JournalEntropy
Volume20
Issue number6
DOIs
StatePublished - Jun 1 2018

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combustion chambers
inference
factorization
economics
regression analysis
fusion
tensors
predictions
simulation

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

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Bayesian nonparametric modeling of categorical data for information fusion and causal inference. / Xiong, Sihan; Fu, Yiwei; Ray, Asok.

In: Entropy, Vol. 20, No. 6, 396, 01.06.2018.

Research output: Contribution to journalArticle

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