Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
-
be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing high
-
networks, Bayesian inference, computational neuroscience, mathematics.
-
the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based
-
to statistical computing, Bayesian modeling, causal inference, clinical trials and analysis of complex large-scale data such as omics data, wearable tech, and electronic health record, with specific preference
-
an equal-variance signal detection model to a more flexible unequal-variance model in a hierarchical Bayesian approach (Lages, 2024). Techniques used: Computational modelling, Bayesian inference, sampling
-
of interest include: infectious disease dynamics in wild and domestic animal populations wildlife diseases and conservation network analysis of disease spread phylodynamics model-based statistical inference
-
guiding materials measurement experiments to acclerate learning the synthesis-process-structure-property relationship. Machine learning methods include, but are not limited to, Bayesian inference
-
nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
-
back at least as far as 1954 (Dowe, 2008a, sec. 1, pp549-550). Discussion of how to do this using the Bayesian information-theoretic minimum message length (MML) approach (Wallace and Boulton, 1968