A Useful Convergence Theorem for Probability Distributions

Probability Distributions and Gleason’s Theorem

We discuss concrete examples for frame functions and their associated density operators, as well as for non-Gleason type probability measures.

A Bernstein-Von Mises Theorem for discrete probability distributions

We investigate the asymptotic normality of the the posterior distribution in the discrete case , when model dimension increases with sample size. We consider a probability mass function θ0 on N \ {0} and a sequence of trunction levels (kn)n satisfying k n ≤ n infi≤kn θ0(i). Then, under some mild conditions on θ0 and on the sequence of prior probabilities on the kn-dimensional simplices. Let θ̂ d...

Convergence of Expected Utilities with Algorithmic Probability Distributions

We consider an agent interacting with an unknown environment. The environment is a function which maps natural numbers to natural numbers; the agent's set of hypotheses about the environment contains all such functions which are computable and compatible with a finite set of known input-output pairs, and the agent assigns a positive probability to each such hypothesis. We do not require that th...

A Convergence Theorem For Controlled . . .

Strong convergence theorem for a class of multiple-sets split variational inequality problems in Hilbert spaces

In this paper, we introduce a new iterative algorithm for approximating a common solution of certain class of multiple-sets split variational inequality problems. The sequence of the proposed iterative algorithm is proved to converge strongly in Hilbert spaces. As application, we obtain some strong convergence results for some classes of multiple-sets split convex minimization problems.