[HTML][HTML] Decision programming for mixed-integer multi-stage optimization under uncertainty
Influence diagrams are widely employed to represent multi-stage decision problems in
which each decision is a choice from a discrete set of alternative courses of action, uncertain …
which each decision is a choice from a discrete set of alternative courses of action, uncertain …
[HTML][HTML] cegpy: Modelling with chain event graphs in Python
Chain event graphs (CEGs) are a recent family of probabilistic graphical models that
generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able …
generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able …
A Bayesian Approach to Infer the Sustainable Use of Artificial Reefs in Fisheries and Recreation
The presence of artificial reefs (ARs) in the south of Portugal that were deployed a few
decades ago and the corroboration of fishing patterns and other activities related to the use …
decades ago and the corroboration of fishing patterns and other activities related to the use …
Optimal sequence of tests for the mediastinal staging of non-small cell lung cancer
Background Non-small cell lung cancer (NSCLC) is the most prevalent type of lung cancer
and the most difficult to predict. When there are no distant metastases, the optimal therapy …
and the most difficult to predict. When there are no distant metastases, the optimal therapy …
Cost-effectiveness analysis with unordered decisions
Introduction Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine
whether the health benefit of an intervention is worth the economic cost. Decision trees, the …
whether the health benefit of an intervention is worth the economic cost. Decision trees, the …
Cost-effectiveness of severe acute malnutrition treatment delivered by community health workers in the district of Mayahi, Niger
EM Molanes-López, JM Ferrer, AO Dougnon… - Human Resources for …, 2024 - Springer
Background A non-randomized controlled trial, conducted from June 2018 to March 2019 in
two rural communes in the health district of Mayahi in Niger, showed that including …
two rural communes in the health district of Mayahi in Niger, showed that including …
Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework
A strong innovative tendency is nowadays emerging that largely comprises new
hydrological modelling approaches, based on Causal Reasoning through Probabilistic …
hydrological modelling approaches, based on Causal Reasoning through Probabilistic …
[PDF][PDF] Synthesis of Strategies in Influence Diagrams.
Influence diagrams (IDs) are a powerful tool for representing and solving decision problems
under uncertainty. The objective of evaluating an ID is to compute the expected utility and an …
under uncertainty. The objective of evaluating an ID is to compute the expected utility and an …
OpenMarkov, an open-source tool for probabilistic graphical models
OpenMarkov is a Java open-source tool for creating and evaluating probabilistic graphical
models, including Bayesian networks, influence diagrams, and some Markov models. With …
models, including Bayesian networks, influence diagrams, and some Markov models. With …
Cost-effectiveness analysis with influence diagrams
Background: Cost-effectiveness analysis (CEA) is used increasingly in medicine to
determine whether the health benefit of an intervention is worth the economic cost. Decision …
determine whether the health benefit of an intervention is worth the economic cost. Decision …