Dynamic Precise and Imprecise Probability Kinematics

2021/10/08 by Michele Caprio, Caprio, Michele, Ruobin Gong +1 · 2 citations
Computer Science · #60A99 #62A01 #Bayesian Modeling and Causal Inference #FOS: Mathematics #Logic, Reasoning, and Knowledge #Probability (math.PR) #Rough Sets and Fuzzy Logic #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2110.04382

openalex publication_date 2021/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce dynamic probability kinematics (DPK), a method for an agent to mechanically update subjective beliefs in the presence of partial information. We then generalize DPK to dynamic imprecise probability kinematics (DIPK), which allows the agent to express their initial beliefs via a set of probabilities in order to further take ambiguity into account. We provide bounds for the lower probability associated with the updated probability sets, and we study the behavior of the latter, in particular contraction, dilation, and sure loss. Examples are provided to illustrate how the methods work.

Citations

• Ergodic Theorems for Lower Probabilities

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