作为不充分理由原理之推论的条件概率公式

作为不充分理由原理推论的独立事件概率定义公式

Definition formula for probabilities of independent events as a consequence of the Insufficient Reason Principle

本研究将独立事件概率的标准定义公式推导为不充分理由原理的推论，该原理以用于全序集分级函数的最大相对散度原理形式呈现。

Abductive, Causal, and Counterfactual Conditionals Under Incomplete Probabilistic Knowledge

We study abductive, causal, and non-causal conditionals in indicative and counterfactual formulations using probabilistic truth table tasks under incomplete probabilistic knowledge (N = 80). We frame the task as a probability-logical inference problem. The most frequently observed response type across all conditions was a class of conditional event interpretations of conditionals; it was followed by conjunction interpretations. An interesting minority of participants neglected some of the relevant imprecision involved in the premises when inferring lower or upper probability bounds on the targ…

Boolean algebras of conditionals, probability and logic

This paper presents an investigation on the structure of conditional events and on the probability measures which arise naturally in this context. In particular we introduce a construction which defines a (finite) {\em Boolean algebra of conditionals} from any (finite) Boolean algebra of events. By doing so we distinguish the properties of conditional events which depend on probability and those which are intrinsic to the logico-algebraic structure of conditionals. Our main result provides a way to regard standard two-place conditional probabilities as one-place probability functions on condit…

Probabilities of conditionals and previsions of iterated conditionals

We analyze selected iterated conditionals in the framework of conditional random quantities. We point out that it is instructive to examine Lewis's triviality result, which shows the conditions a conditional must satisfy for its probability to be the conditional probability. In our approach, however, we avoid triviality because the import-export principle is invalid. We then analyze an example of reasoning under partial knowledge where, given a conditional if $A$ then $C$ as information, the probability of $A$ should intuitively increase. We explain this intuition by making some implicit backg…