Efficient Inference for Coupled Hidden Markov Models in Continuous Time and Discrete Space.

Giosue Migliorini, Padhraic Smyth

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2026. 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

2 authors.

Giosue MiglioriniDepartment of Statistics, University of California, Irvine.

Padhraic SmythDepartment of Computer Science, University of California, Irvine.

Funding

Personalized Risk Predictions with Deep Learning Methods in the Presence of Missing and Biased Electronic Health Record DataR01LM013344 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI SMYTH, PADHRAIC, ZHONG, HUA JUDY · 2021 to 2024

$1.3M

SCH: Individualized learning and prediction for heterogeneous multimodal data from wearable devicesR01CA297869 · NCI · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI Yuqing Guo, Annie Qu · 2024 to 2026

$900k

NCI NIH HHS R01 CA297869NLM NIH HHS R01 LM013344

6 · The paper itself

Abstract

Systems of interacting continuous-time Markov chains are a powerful model class, but inference is typically intractable in high-dimensional settings. Auxiliary information, such as noisy observations, is typically only available at discrete times, and incorporating it via a Doob's

Identifiers

PMID42751685

PMCPMC13580705

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.