Nayak, Pattanayak and Mishra [1] proved that the random Fourier-Stieltjes series(RFS) (1 ) converges in probability to the

stochastic integralSince the

stochastic integral on the right-hand side of Eq.

2]), in paper [1] we could avoid the use of the

stochastic integral to obtain some results from stochastic calculus, e.

X [OMEGA] [right arrow] X, x [member of] X, satisfying the

stochastic integral equation:

He will present, "FastSies: A Fast

Stochastic Integral Equation Solver for Modeling the Rough Surface Effects," during the Silicon Valley Chapter of the IEEE Solid State Circuits Society Monday, May 15, at 7 p.

The forward looking

stochastic integral in discrete time of a process H with respect to a process S is defined by

The proposition below tells us that the sum of an accumulated discounted consumption process and its corresponding discount wealth process can be expressed as a

stochastic integral with respect to the Brownian motion.

1]), we do not make use of the

stochastic integral.

This calculus exhibits a number of novel features, and McKean finds many in his coverage of Brownian motion, including the issue of Martingale inequality and the law of the iterated logarithm,

stochastic integrals and differentials, including Wiener's and Ito's definitions of the

stochastic integral, time substitution, and

stochastic integrals and differentials for several-dimensional Brownian motion,

stochastic integral equations in which d=1, including Lampertoi's methods and Feller's test for explosions, and when d is greater than or equal to 2, including Weyl's Lemma and Brownian motions in a Lie group.

This endeavor entails developing new notions of nonlinear

stochastic integrals, and requires a theory that looks beyond the established setting of semimartingales.

Compared to the methods based on Taylor expansion, the proposed symplectic weak second-order methods are implicit, but they are comparable in terms of the number and the complexity of the multiple Ito

stochastic integrals or the derivatives of the Hamiltonian functions required.

After a description of the Poisson process and related processes with independent increments, as well as a brief look at Markov processes with a finite number of jumps, the book introduces Brownian motion and develops

stochastic integrals and Ito's theory in the context of one-dimensional diffusion processes.