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Brusa, L., Pennoni, F., Bartolucci, F., Peruilh Bagolini, R. (2024). Addressing latent state separation in hidden Markov models for categorical data with covariates: A penalised maximum likelihood approach. In CHALLENGES IN CATEGORICAL DATA ANALYSIS LSE 2024 BOOK OF ABSTRACTS.

Addressing latent state separation in hidden Markov models for categorical data with covariates: A penalised maximum likelihood approach

Brusa, L;Pennoni, F;
2024
abstract + slide
Binary longitudinal data, Discrete latent variables, Early-warning system, Expectation-Maximization algorithm, Hypotension data, Penalized likelihood
English
Challenges for Categorical Data Analysis (CCDA 2024) - October 31 to November 1st, 2024
2024
CHALLENGES IN CATEGORICAL DATA ANALYSIS LSE 2024 BOOK OF ABSTRACTS
2024
https://sites.google.com/view/ccda2024/talks-slides
open
Brusa, L., Pennoni, F., Bartolucci, F., Peruilh Bagolini, R. (2024). Addressing latent state separation in hidden Markov models for categorical data with covariates: A penalised maximum likelihood approach. In CHALLENGES IN CATEGORICAL DATA ANALYSIS LSE 2024 BOOK OF ABSTRACTS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/523120
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