DOAJ Open Access 2018

Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference

Sihan Xiong Yiwei Fu Asok Ray

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Science Astrophysics Physics

Penulis (3)

Sihan Xiong

Yiwei Fu

Asok Ray

Format Sitasi

APA MLA BibTeX

Xiong, S., Fu, Y., Ray, A. (2018). Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference. https://doi.org/10.3390/e20060396

Akses Cepat

Lihat di Sumber doi.org/10.3390/e20060396

Informasi Jurnal

Tahun Terbit: 2018
Sumber Database: DOAJ
DOI: 10.3390/e20060396
Akses: Open Access ✓