A plausibility-augmented imprecise-probability model updates via sampling (plausibilistic Bayes) or inequalities and proves almost-sure convergence of beliefs to the true probability.
Decision making under uncertainty using imprecise probabilities
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
A new algorithm for finding maximal gambles outperforms two existing ones across all tested scenarios on randomly generated decision problems with lower previsions.
citing papers explorer
-
Learning Probabilities: Towards a Logic of Statistical Learning
A plausibility-augmented imprecise-probability model updates via sampling (plausibilistic Bayes) or inequalities and proves almost-sure convergence of beliefs to the true probability.
-
Improving and benchmarking of algorithms for decision making with lower previsions
A new algorithm for finding maximal gambles outperforms two existing ones across all tested scenarios on randomly generated decision problems with lower previsions.