Nonparametric predictive inference and interval probability

This paper presents the unique position of A(n)-based nonparametric predictive inference within the theory of interval probability. It provides a completely new understanding, leading to powerful new results and a well-founded justification of such inferences by proving strong internal consistency results

Combining Non-probability and Probability Survey Samples Through Mass Imputation

This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sample matching without rigorous theoretical justification. Under suitable regularity conditions, we establish the consistency of the mass imputation estimator and derive its asymptotic variance formula. Variance estimators are developed using either linearization or bootstrap. Finite sample performances of the mass imputation estimator are investigated through simulation studies and an application to analyzing a non-probability sample collected by the Pew Research Centre.Comment: Submitted to Journal of the Royal Statistical Society: Series

Merging the local and global approaches to probabilistic satisfiability

AbstractThe probabilistic satisfiability problem is to verify the consistency of a set of probability values or intervals for logical propositions. The (tight) probabilistic entailment problem is to find best bounds on the probability of an additional proposition. The local approach to these problems applies rules on small sets of logical sentences and probabilities to tighten given probability intervals. The global approach uses linear programming to find best bounds. We show that merging these approaches is profitable to both: local solutions can be used to find global solutions more quickly through stabilized column generation, and global solutions can be used to confirm or refute the optimality of the local solutions found. As a result, best bounds are found, together with their step-by-step justification

Subset models for justification logic

We introduce a new semantics for justification logic based on subset relations. Instead of using the established and more symbolic interpretation of justifications, we model justifications as sets of possible worlds. We introduce a new justification logic that is sound and complete with respect to our semantics. Moreover, we present another variant of our semantics that corresponds to traditional justification logic. These types of models offer us a versatile tool to work with justifications, e.g.~by extending them with a probability measure to capture uncertain justifications. Following this strategy we will show that they subsume Artemov's approach to aggregating probabilistic evidence

Epistemic Akrasia and Epistemic Reasons

It seems that epistemically rational agents should avoid incoherent combinations of beliefs and should respond correctly to their epistemic reasons. However, some situations seem to indicate that such requirements cannot be simultaneously satisfied. In such contexts, assuming that there is no unsolvable dilemma of epistemic rationality, either (i) it could be rational that one’s higher-order attitudes do not align with one’s first-order attitudes or (ii) requirements such as responding correctly to epistemic reasons that agents have are not genuine rationality requirements. This result doesn’t square well with plausible theoretical assumptions concerning epistemic rationality. So, how do we solve this puzzle? In this paper, I will suggest that an agent can always reason from infallible higher-order reasons. This provides a partial solution to the above puzzle

Bayesian Probabilities and the Histories Algebra

We attempt a justification of a generalisation of the consistent histories programme using a notion of probability that is valid for all complete sets of history propositions. This consists of introducing Cox's axioms of probability theory and showing that our candidate notion of probability obeys them. We also give a generalisation of Bayes' theorem and comment upon how Bayesianism should be useful for the quantum gravity/cosmology programmes.Comment: 10 pages, accepted by Int. J. Theo. Phys. Feb 200