6,844 research outputs found
An original and additional mathematical model characterizing a Bayesian approach to decision theory
We propose an original mathematical model according to a Bayesian approach explaining uncertainty from a
point of view connected with vector spaces. A parameter space can be represented by means of random quantities by
accepting the principles of the theory of concordance into the domain of subjective probability. We observe that metric
properties of the notion of -product mathematically fulfill the ones of a coherent prevision of a bivariate random quantity.
We introduce fundamental metric expressions connected with transformed random quantities representing changes of
origin. We obtain a posterior probability law by applying the Bayes’ theorem into a geometric context connected with a
two-dimensional parameter space
Floating-Point Matrix Product on FPGA
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Distances between States and between Predicates
This paper gives a systematic account of various metrics on probability
distributions (states) and on predicates. These metrics are described in a
uniform manner using the validity relation between states and predicates. The
standard adjunction between convex sets (of states) and effect modules (of
predicates) is restricted to convex complete metric spaces and directed
complete effect modules. This adjunction is used in two state-and-effect
triangles, for classical (discrete) probability and for quantum probability
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