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

Sihan Xiong, Yiwei Fu, Asok Ray

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sihan XiongDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802-1412, USA.

Yiwei FuDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802-1412, USA.

Asok RayDepartment of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802-1412, USA.

Funding

U.S. Air Force Office of Scientific Research (AFOSR) FA9550-15-1-0400

6 · The paper itself

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.

Indexed as

Bayes factorBayesian nonparametriccausal inferenceconditional tensor factorizationinformation fusionsequential classificationthermoacoustic instability

Identifiers

PMID33265485

DOI10.3390/e20060396

PMCPMC7512915

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.