I’m developing an account of objective Bayesianism. This is intended as a theory of rational degree of belief, rather than a theory of statistical inference.

The account is distinctive in that:

- It rejects the usual Bayesian identification of conditional degree of belief with conditional probability.
- It doesn’t suppose that degrees of belief should be updated by Bayesian conditionalisation.
- It rejects a common objective Bayesian supposition that evidence uniquely determines a rational belief function.
- It takes objective chances to play a central role in determining rational degrees of belief.

The account is built on three kinds of norm:

- Structural. An agent’s belief function should be a probability function.
- Evidential. If the agent establishes from evidence that the chance function is in some set of probability functions, then her belief function should be in the convex hull of that set.
- Equivocation. The agent’s degrees of belief should otherwise be equivocal, adopting committal degrees of belief (near 0 or 1) only where they are forced by structural or evidential norms.

**Motivation**

For some recent arguments for this sort of approach, see:

Jon Williamson: **A Bayesian account of establishing**, *British Journal for the Philosophy of Science*, 2022. . doi: 10.1086/714798

Jon Williamson: **Direct inference and probabilistic accounts of induction**, *Journal for General Philosophy of Science*, 2022. . doi: 10.1007/s10838-021-09584-0

For an introduction to the approach, see:

Jon Williamson: **In defence of objective Bayesianism**, Oxford University Press, 2010.

**Objective Bayesian inductive logic**

I’m also interested in the use of Objective Bayesianism to provide a new approach to inductive logic. I’m currently collaborating with Juergen Landes and Soroush Rafiee Rad on this.

Recent work includes:

Juergen Landes, Soroush Rafiee Rad and Jon Williamson: **Determining maximal entropy functions for objective Bayesian inductive logic**, *Journal of Philosophical Logic, *in press.

Juergen Landes, Soroush Rafiee Rad and Jon Williamson: **Towards the Entropy-Limit Conjecture**, *Annals of Pure and Applied Logic* 172(2):102870, 2021. . doi: 10.1016/j.apal.2020.102870

For an introduction to the approach, see:

Jon Williamson: **Lectures on inductive logic**, Oxford University Press, 2017. Errata.

**Objective Bayesian nets**

Graphical models can be used to represent and reason with objective Bayesian probabilities.

Recent work includes:

Juergen Landes and Jon Williamson: **Objective Bayesian nets for integrating consistent datasets**, *Journal of Artificial Intelligence Research* 74: 393-458, 2022. . doi 10.1613/jair.1.13363

For an introduction, see:

Rolf Haenni, Jan-Willem Romeijn, Gregory Wheeler & Jon Williamson: **Probabilistic logics and probabilistic networks**, Synthese Library, Springer, 2011.

Jon Williamson: **Bayesian nets and causality: philosophical and computational foundations**, Oxford University Press, 2005.