Conditional Probabilistic Population Forecasting

Conditional Probabilistic Population Projections: An Application to Climate Change

Conditional probabilistic reasoning without conditional logic

Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revision functions, Standard Imaging and General Imaging, have successfully been applied to modelling information retrieval (IR). Due to the problematic nature of a“direct” implementation of Imaging revision functions, we propose the...

Consensus Probabilistic Forecasting

To make optimal decisions, end-users of decision support systems require information accurately describing the uncertainty of the underlying weather forecasts. Air temperature, dew point temperature, and wind speed are critical surface weather variables in many economic sectors. The generation of sharp and calibrated probabilistic forecasts and their effective presentation to decision makers ar...

Probabilistic Conditional Independence Structuresand

1 Franti sek Mat u s Prague ABSTRACT. Special conditional independence structures have been recognized to be matroids. This opens new possibilities for the application of matroid theory methods (duality, minors, expansions) to the study of conditional independence and, on the other hand, starts a new probabilistic branch of matroid representation theory. 2

Probabilistic Conditional Preference Networks

This paper proposes a “probabilistic” extension of conditional preference networks as a way to compactly represent a probability distributions over preference orderings. It studies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from the algorithmical and complexity viewpoints. Efficient algorithms for tree-structured probabilistic CP-nets a...