About: Conditional event algebra is a research topic. Over the lifetime, 183 publications have been published within this topic receiving 6110 citations.
TL;DR: The truthful speaker wants not to assert falsehoods, wherefore he is willing to assert only what he takes to be very probably true as discussed by the authors, where assertability goes by subjective probability.
Abstract: The truthful speaker wants not to assert falsehoods, wherefore he is willing to assert only what he takes to be very probably true. He deems it permissible to assert that A only if P(A) is sufficiently close to 1, where P is the probability function that represents his system of degrees of belief at the time. Assertability goes by subjective probability.
TL;DR: It is proved that every probability assignment has uncountably many ‘trouble spots’, andConditional probability should be taken as the primitive notion, and unconditional probabilityshould be analyzed in terms of it.
Abstract: Kolmogorov's axiomatization of probability includes the familiarratio formula for conditional probability:
$$({\text{RATIO}}) P(A|B) = \frac{{P(A \cap B)}}{{P(B)}}{\text{ }}(P(B) >0).$$
Call this the ratio analysis of conditional probability. It has become so entrenched that it is often referred to as the definition of conditional probability.I argue that it is not even an adequate analysis of that concept. I prove what I call the Four Horn theorem, concluding that every probability assignment has uncountably many ‘trouble spots’. Trouble spots come in four varieties: assignments of zero togenuine possibilities; assignments of infinitesimals to such possibilities; vague assignments to such possibilities; and no assignment whatsoever to such possibilities. Each sort of trouble spot can create serious problems for the ratio analysis. I marshal manyexamples from scientific and philosophical practice against the ratio analysis. I conclude more positively: we should reverse the traditional direction of analysis. Conditional probability should be taken as the primitive notion, and unconditional probability should be analyzed in terms of it. “I'd probably be famous now If I wasn't such a good waitress.”
Jane Siberry, “Waitress”
TL;DR: This work defines independence via orthogonality in information space so that it can explicitly describe the kind of dependence that occurs between P"Y and P"X"|"Y making the causal hypothesis ''Y causes X'' implausible.