Ambiguity reduction through new statistical data

Ambiguity Reduction Through new Statistical Data

We provide some objective foundations for a belief revision process in a situation where (i) the decision-maker's initial probabilistic knowledge is imprecise and characterized by the core of a belief function, (ii) expected new data are themselves consistent with a belief function with known focal sets and (iii) the revision process is based on belief function combination. We study the propert...

Statistical Decisions under Ambiguity

Consider a decision maker who faces a number of possible models of the world. Every model generates objective probabilities, but no probabilities of models are given. This is the classic setting of statistical decision theory; recent and less standard applications include decision making with model uncertainty, e.g. due to concerns for misspecification, treatment choice with partial identificat...

The Ambiguity of Reduction

I claim that the question of whether chemistry is reduced to quantum mechanics is more ambiguous and multi-faceted than generally supposed. For example, chemistry appears to be both reduced and not reduced at the same time depending on the perspective that one adopts. Similarly, I argue that some conceptual issues in quantum mechanics are ambiguous and can only be laid to rest by embracing para...

Statistical Inference Through Data Compression

Data reduction through early grouping

SQL queries containing GROUP BY and aggre-gation occur frequently in decision support applications. Grouping with aggregation is typically done by rst sorting the input and then performing the aggregation as part of the output phase of the sort. The most widely used external sorting algorithm is merge sort, consisting of a run formation phase followed by a (single) merge pass. The amount of dat...