Name
Abstract | ||
|---|---|---|
Objective Bayesian probability is often dened over rather simple do- mains, e.g., nite event spaces or propositional languages. This paper investigates the extension of objective Bayesianism to rst-order logical languages. It is argued that the objective Bayesian should choose a prob- ability function, from all those that satisfy constraints imposed by back- ground knowledge, that is closest to a particular frequency-induced proba- bility function which generalises the = 0 function of Carnap's continuum of inductive methods. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11229-007-9298-y | Synthese |
Keywords | Field | DocType |
Bayesianism,Objective Bayesianism,Bayesian epistemology,Formal epistemology,Inductive logic,Probabilistic logic | Formal epistemology,Inductive logic,Computer science,Artificial intelligence,Probabilistic logic,Predicate (grammar),Probability density function,Bayesian probability | Journal |
Volume | Issue | ISSN |
163 | 3 | 0039-7857 |
Citations | PageRank | References |
2 | 0.77 | 4 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jon williamson | 1 | 38 | 11.42 |