Purely Probabilistic Measures of Explanatory Power – A Critique

Comparing Probabilistic Measures of Explanatory Power

Recently, in attempting to account for explanatory reasoning in probabilistic terms, Bayesians have proposed several measures of the degree to which a hypothesis explains a given set of facts. These candidate measures of “explanatory power” are shown to have interesting normative interpretations and consequences. What has not yet been investigated, however, is whether any of these measures are ...

ILLNESS EXPLANATORY MODELS IN CONTEMPORARY RESEARCH: A CRITIQUE OF THE EXPLANATORY MODEL INTERVIEW CATALOGUE by

Probabilistic Networks and Explanatory Coherence

Causal reasoning can be understood qualitatively in terms of explanatory coherence or quantitatively in terms of probability theory. Comparison of these approaches can be done by looking at computational models, using my explanatory coherence networks and Pearl’s probabilistic ones. The explanatory coherence program ECHO can be given a probabilistic interpretation, but there are many conceptual...

Parsimony and explanatory power

Parsimony can be related to explanatory power, either by noting that each additional requirement for a separate origin of a feature reduces the number of observed similarities that can be explained as inheritance from a common ancestor; or else by applying Popper s formula for explanatory power together with the fact that parsimony yields maximum likelihood trees under No Common Mechanism (NCM)...

Quantitative Parsimony and Explanatory Power

The desire to minimize the number of individual new entities postulated is often referred to as quantitative parsimony. Its influence on the default hypotheses formulated by scientists seems undeniable. I argue that there is a wide class of cases for which the preference for quantitatively parsimonious hypotheses is demonstrably rational. The justification, in a nutshell, is that such hypothese...