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The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning

School of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410003, China
Philosophies 2020, 5(4), 25; https://doi.org/10.3390/philosophies5040025
Received: 8 July 2020 / Revised: 15 September 2020 / Accepted: 16 September 2020 / Published: 2 October 2020
(This article belongs to the Special Issue Logic and Science)
Many researchers want to unify probability and logic by defining logical probability or probabilistic logic reasonably. This paper tries to unify statistics and logic so that we can use both statistical probability and logical probability at the same time. For this purpose, this paper proposes the P–T probability framework, which is assembled with Shannon’s statistical probability framework for communication, Kolmogorov’s probability axioms for logical probability, and Zadeh’s membership functions used as truth functions. Two kinds of probabilities are connected by an extended Bayes’ theorem, with which we can convert a likelihood function and a truth function from one to another. Hence, we can train truth functions (in logic) by sampling distributions (in statistics). This probability framework was developed in the author’s long-term studies on semantic information, statistical learning, and color vision. This paper first proposes the P–T probability framework and explains different probabilities in it by its applications to semantic information theory. Then, this framework and the semantic information methods are applied to statistical learning, statistical mechanics, hypothesis evaluation (including falsification), confirmation, and Bayesian reasoning. Theoretical applications illustrate the reasonability and practicability of this framework. This framework is helpful for interpretable AI. To interpret neural networks, we need further study. View Full-Text
Keywords: statistical probability; logical probability; semantic information; rate-distortion; Boltzmann distribution; falsification; verisimilitude; confirmation measure; Raven Paradox; Bayesian reasoning statistical probability; logical probability; semantic information; rate-distortion; Boltzmann distribution; falsification; verisimilitude; confirmation measure; Raven Paradox; Bayesian reasoning
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Lu, C. The P–T Probability Framework for Semantic Communication, Falsification, Confirmation, and Bayesian Reasoning. Philosophies 2020, 5, 25.

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