It provides a state-of-the-art overview of the theoretical underpinnings of modern probabilistic programming and their applications in machine learning, security, and other domains, at a level suitable for grad
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Davidson-Pilon, C. (2015).Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference. Addison-Wesley Professional. 7. McElreath, R. (2015).Statistical Rethinking: A Bayesian Course With Examples in R and Stan. Chapman & Hall/CRC. 8. Hastie, T., Tibshirani, R., & Friedm...
Later on we shall see probabilistic analogues of these two basic results (Theorem 3, Theorem 6), involving convex compact Hausdorff spaces, see Theorem 24, Theorem 25. 2.3. The continuation monad The continuation monad is useful in the context of programming semantics, where it is employed for...
Programming Languages Computational Number Theory Cryptography Database Theory Queuing Methods Distributed &High Performance Computing Computer Security Program Semantics and Logic Probabilistic Methods Computation Biology Internet & Cloud Computing Software Engineering ...
Over the years, a number of theoretical aspects of reversible computing have been studied, dealing with categorical foundations of reversibility, founda- tions of programming languages and term rewriting, considering various models of sequential (automata, Turing machines) and concurrent (cellular ...
Programming Languages Computational Number Theory Cryptography Database Theory Queuing Methods Distributed & High Performance Computing Computer Security Program Semantics and Logic Probabilistic Methods Computation Biology Internet & Cloud Computing Software Engineering Artificial Intelligence Biochemistry Astrophysics Ge...
A useful tool in understanding the roots of the world geography of culture is the Age-Area-Hypothesis. The Age-Area Hypothesis (AAH) asserts that the point of geographical origin of a group of related cultures is most likely where the culture speaking th
PhD thesis, University of California at Berkeley, 1991.Michelena, N. and A.M. Agogino (1992b), "Monotonic Influence Diagrams: Extension to Stochastic Programmingand Application to Probabilistic Design", submitted to Engineering OptimizationN. Michelena and A. Agogino, "Monotonic influence diagrams:...
This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship...