Name
Abstract | ||
|---|---|---|
Inductive logic admits a variety of semantics (Haenni et al. (2011) [7, Part 1]). This paper develops semantics based on the norms of Bayesian epistemology (Williamson, 2010 [16, Chapter 7]). Section 1 introduces the semantics and then, in Section 2, the paper explores methods for drawing inferences in the resulting logic and compares the methods of this paper with the methods of Barnett and Paris (2008) [2]. Section 3 then evaluates this Bayesian inductive logic in the light of four traditional critiques of inductive logic, arguing (i) that it is language independent in a key sense, (ii) that it admits connections with the Principle of Indifference but these connections do not lead to paradox, (iii) that it can capture the phenomenon of learning from experience, and (iv) that while the logic advocates scepticism with regard to some universal hypotheses, such scepticism is not problematic from the point of view of scientific theorising. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jal.2013.03.006 | Journal of Applied Logic |
Keywords | Field | DocType |
Bayesianism,Objective Bayesianism,Bayesian epistemology,Maximum entropy,Maxent,Inductive logic,Probabilistic logic,Probability logic | Computational logic,Computer science,Multimodal logic,Term logic,Algorithm,Philosophy of logic,Epistemology,Principle of bivalence,Many-valued logic,Predicate logic,Higher-order logic | Journal |
Volume | Issue | ISSN |
11 | 4 | 1570-8683 |
Citations | PageRank | References |
3 | 0.59 | 4 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| jon williamson | 1 | 38 | 11.42 |