Today there’s an abundance of textbooks and webbooks on Bayesian probability theory, decision theory, and statistics, at very diverse technical levels. I wanted to point out three books whose main topic is not probability theory, but which give very good introductions (even superior to those of some specialized textbooks, in my opinion) to Bayesian probability theory:

  • Artificial Intelligence: A Modern Approach by S. J. Russell, P. Norvig. Part IV is an amazing introduction to Bayesian theory – including decision theory – with many connections with Artificial Intelligence and Logic.

  • Medical Decision Making by H. C. Sox, M. C. Higgins, D. K. Owens. This is essentially a very clear and insightful textbook on Bayesian probability theory and decision theory, but targeted to clinical decision-making.

  • Sentential Probability Logic: Origins, Development, Current Status, and Technical Applications by T. Hailperin. This is a book on Bayesian probability theory, presented as a generalization of propositional logic. This point of view is the most powerful I know of. The books also has important results on methods to find probability bounds, and on combining evidence.

  • SalamanderAEnglish
    2·
    1 year ago

    Extreme example: even if we give quadrillions of training data about an ordinary tossed coin to some algorithm, the algorithm will never be able to get more than 50% right at the next toss.

    This makes me think about stock traders who are trying to build AI models to optimize their trading strategy. I can’t say that they are wrong, and sure, why not, I encourage them to try… But I think they are dealing with a very similar problem to a coin toss.

    Well, sorry for the babble – good luck with the research!

    Oh, don’t apologize for that!! And thank you :D