Bayesian Epistemology: Perspectives and Challenges
Bayesian epistemology remains the dominant account of rational beliefs, it underpins the dominant account of decision making in science and beyond, as well as many of our statistical methods.
While important applications continue to to emerge, the work on the foundations of Bayesian epistemology never stops and a number of challenges are emerging.
The aim of this conference is bring together scholars exploring applications, challenges and foundations of Bayesian epistemology.
Topics of interest (in alphabetic order) are not limited to:
- Accuracy
- Bayesianism and Artificial Intelligence
- Bayesian Networks
- Bounded Rationality
- Causation
- Confirmation
- Disagreement
- Evidence
- Evidence Aggregation
- Expansion
- Foundational Aspects of Bayesian Statistics
- Higher Order Evidence
- Imprecise Bayesian Approaches
- Induction
- Inference
- Interpretations of Probabilities
- Judgement Aggregation
- Maximum Entropy (Applications, Inference and Methods)
- Multi Agent Epistemology
- Objective Bayesian Epistemology
- Principles of Bayesianism (Conditionalisation, Probabilism, Total Evidence)
- Replication
- Updating Procedures (Jeffrey, KL, L&P).