Results 181 to 190 of about 123,391 (326)

Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models

open access: yesBritish Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract Hidden Markov diagnostic classification models capture how students' cognitive attributes evolve over time. This paper introduces a Bayesian Markov chain Monte Carlo algorithm for diagnostic classification models that jointly estimates time‐varying Q matrices, latent attributes, item parameters, attribute class proportions and transition ...
Chen‐Wei Liu
wiley   +1 more source

LLM‐based prior elicitation for Bayesian graphical modeling

open access: yesBritish Journal of Mathematical and Statistical Psychology, EarlyView.
ABSTRACT In the Bayesian graphical modeling framework, priors on network structure encode theoretical assumptions and uncertainty about the topology of psychological constructs under study. For instance, the Bernoulli prior specifies the probability of each pairwise interaction, the Beta–Bernoulli prior governs expected network density, and the ...
Nikola Sekulovski   +2 more
wiley   +1 more source

Spatial Transcriptomics and Bulk RNA‐Seq Analysis Revealed Molecular Classification of Invasive Lobular Carcinoma

open access: yesCancer Science, EarlyView.
Spatial transcriptomics and bulk RNA‐seq data analysis revealed the molecular characteristics of invasive lobular carcinoma (ILC). ILC was classified into three clusters: proliferative, immunoreactive, and stromal‐rich, with different prognoses. ABSTRACT Invasive lobular carcinoma (ILC) is a special type of breast cancer.
Momoko Tokura   +9 more
wiley   +1 more source

Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution. [PDF]

open access: yesBMC Bioinformatics, 2013
Galligan MC   +4 more
europepmc   +1 more source

Hierarchical Differentiable Fluid Simulation

open access: yesComputer Graphics Forum, EarlyView.
We introduce a two‐step algorithm that significantly reduces memory usage for solving control problems using differentiable fluid simulation techniques: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization
Xiangyu Kong   +4 more
wiley   +1 more source

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