94 citations · 165 across the 11 of their papers we have counts for
6 papers · 1 filter
Approximating Euclidean by Imprecise Markov Decision Processes
Manfred Jaeger, Giorgio Bacci, Giovanni Bacci +2
Euclidean Markov decision processes are a powerful tool for modeling control problems under uncertainty over continuous domains. Finite state imprecise, Markov decision processes c…
Inference, Learning, and Population Size: Projectivity for SRL Models
Manfred Jaeger, Oliver Schulte
A subtle difference between propositional and relational data is that in many relational models, marginal probabilities depend on the population or domain size. This paper connects…
A Logic for Default Reasoning About Probabilities
Manfred Jaeger
A logic is defined that allows to express information about statistical probabilities and about degrees of belief in specific propositions. By interpreting the two types of probabi…
Relational Bayesian Networks
Manfred Jaeger
A new method is developed to represent probabilistic relations on multiple random events. Where previously knowledge bases containing probabilistic rules were used for this purpose…
Measure Selection: Notions of Rationality and Representation Independence
Manfred Jaeger
We take another look at the general problem of selecting a preferred probability measure among those that comply with some given constraints. The dominant role that entropy maximiz…
Lower Complexity Bounds for Lifted Inference
Manfred Jaeger
One of the big challenges in the development of probabilistic relational (or probabilistic logical) modeling and learning frameworks is the design of inference techniques that oper…