Logical Prior Probability
نویسنده
چکیده
A Bayesian prior over first-order theories is defined. It is shown that the prior can be approximated, and the relationship to previously studied priors is examined.
منابع مشابه
'Plausibilities of plausibilities': an approach through circumstances
Probability-like parameters appearing in some statistical models, and their prior distributions, are reinterpreted through the notion of `circumstance', a term which stands for any piece of knowledge that is useful in assigning a probability and that satisfies some additional logical properties. The idea, which can be traced to Laplace and Jaynes, is that the usual inferential reasonings about ...
متن کاملResurrecting Logical Probability
The logical interpretation of probability, or “objective Bayesianism” – the theory that (some) probabilities are strictly logical degrees of partial implication – is defended. The main argument against it is that it requires the assignment of prior probabilities, and that any attempt to determine them by symmetry via a “principle of insufficient reason” inevitably leads to paradox. Three replie...
متن کاملThe Framing and Evaluation of Multiple Hypotheses*
This study provides exploratory evidence on auditors’ framing and evaluation of hypotheses, identifies implications for improving audit decision-making and facilitates the interpretation of prior research. Prior studies usually assume hypotheses to be framed as mutually exclusive and exhaustive. However, both verbal protocol evidence and probability assessments reveal that in a realistic case m...
متن کاملPrior Probabilities I. Background of the Problem
In decision theory, mathematical analysis shows that once the sampling distributions, loss function, and sample are speci ed, the only remaining basis for a choice among di erent admissible decisions lies in the prior probabilities. Therefore, the logical foundations of decision theory cannot be put in fully satisfactory form until the old problem of arbitrariness (sometimes called \subjectiven...
متن کاملData Analysis Project: A Probabilistic Generative Grammar for Semantic Parsing
We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical fo...
متن کامل