Stochastic Systems: Modeling, Identification and Optimization, I

Chapter 1 Extension of Clark’s innovations equivalence theorem to the case of signal z . independent of noise, with ∫0t z s2ds<∞ a.s.

Chapter 2 Parametrically stochastic linear differential equations

Chapter 3 Entrance-exit distributions for Markov additive processes

Chapter 4 Martingales of a jump process and absolutely continuous changes of measure

Chapter 5 Analysis of Brownian functionals

Chapter 6 Probabilistic representations of boundary layer expansions

Chapter 7 Limit theorems and diffusion approximations for density dependent Markov chains

Chapter 8 The choice of a stochastic model for a noise system

Chapter 9 Asymptotic stability and angular convergence of stochastic systems

Chapter 10 Remarks on wide sense equivalents of continuous Gauss-Markov processes in Rn

Chapter 11 A reduced dimensionality method for the steady-state Kalman filter

Chapter 12 On continuous and discrete sampling for parameter estimation in diffusion type processes

Chapter 13 On integrals in multi-output discrete-time Kalman-Bucy filtering

Chapter 14 On consistency and identifiability

Chapter 15 Identification and estimation of the error-in-variables model (EVM) in structural form

Chapter 16 Value of information in zero-sum games

Chapter 17 Sequential decision and stochastic control