We consider the task of forecasting an infinite sequence of future observations based on some number of past observations, where the probability measure generating the observations is “suspected” to satisfy one or more of a set of incomplete models, i.e. convex sets in the space of probability measures. This setting is in some sense intermediate between the realizable setting where the probabil...

The error bound in probability between the approximate maximum likelihood estimator AMLE and the continuous maximum likelihood estimator MLE is investigated for nonlinear nonhomogenous stochastic system with unknown parameter. The rates of convergence of the approximations for Itô and ordinary integral are introduced under some regular assumptions. Based on these results, the in probability rat...

In this paper we study the convergence of the numerical path integration method which produces approximate probability density functions for the solutions of stochastic differential equations. This method enjoys several advantages compared to path-wise approximation schemes. We prove that the approximate probability densities converge strongly in L1 to the probability density of the stochastic ...

This paper talks about both the asymptotic and non-asymptotic convergence properties of stochastic approximation algorithms with controlled Markov noise when stability of the iterates (which is an important condition for almost sure convergence) is hard to prove. We achieve the same by giving a lower bound on the lock-in probability of such frameworks i.e. the probability of convergence to a sp...

This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound, that holds with high probability, on the excess risk of the output of an online algorithm in terms of the average regret. This allows one to use recent algorithms with logarithmic cumulative regret guarantees to achi...

The real-world applications addressing the nonlinear functions of multiple variables could be implicitly assessed through structural reliability analysis. This study establishes an efficient algorithm for resolving highly nonlinear structural reliability problems. To this end, first a numerical nonlinear optimization algorithm with a new simple filter is defined to locate and estimate the most ...

Weak gravitational lensing surveys have the potential to directly probe mass density fluctuation in the universe. Recent studies have shown that it is possible to model the statistics of the convergence field at small angular scales by modeling the statistics of the underlying density field in the highly nonlinear regime. We propose a new method to model the complete probability distribution fu...

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...

In the present work‎, ‎a new stochastic algorithm is proposed to solve multiple dimensional Fredholm integral equations of the second kind‎. ‎The solution of the‎ integral equation is described by the Neumann series expansion‎. ‎Each term of this expansion can be considered as an expectation which is approximated by a continuous Markov chain Monte Carlo method‎. ‎An algorithm is proposed to sim...

Lyapunov drift and Lyapunov optimization are powerful techniques for optimizing time averages in stochastic queueing networks subject to stability. However, there are various definitions of queue stability in the literature, and the most convenient Lyapunov drift conditions often provide stability and performance bounds only in terms of a time average expectation, rather than a pure time averag...