The standard state-spaces of asymmetric information preclude non-trivial forms of unawareness (Modica and Rustichini, 1994, Dekel, Lipman and Rustichini, 1998). We introduce a generalized state-space model that allows for non-trivial unawareness among several individuals, and which satisfies strong properties of knowledge as well as all the desiderata on unawareness proposed this far in the lit...

Drew Fudenberg and two referees for helpful comments.

In this paper, the one-dimensional Gray–Markel lattice-ladder discrete filter structure is extended to two dimensions (2D). The proposed 2D circuit implementation has minimal number of unit delays. Based on this circuit implementation the corresponding 2D state space realization is derived. The matrices A, b, c and the scalar d of the 2D state space model are presented in generalized closed for...

Through Laplace transforms, we study the extremes of a continuoustime Markov-additive process with one-sided jumps and a finite-state background Markovian state-space, jointly with the epoch at which the extreme is ‘attained’. For this, we investigate discrete-time Markov-additive processes and use an embedding to relate these to the continuous-time setting. The resulting Laplace transforms are...

We consider the problem of implementing simple and efficient Markov chain Monte Carlo (MCMC) estimation algorithms for state space models. A conceptually transparent derivation of the posterior distribution of the states is discussed, which also leads to an efficient simulation algorithm that is modular, scalable, and widely applicable. We also discuss a simple approach for evaluating the integ...

First analysis of Markov Reward Models (MRM) resulted in a double transform expression, whose numerical solution is based on the inverse transformations both in time and reward variable domain. Better numerical methods were proposed based on the time domain properties of these models, such as the set of partial differential equation describes the process evolution in time. This paper introduces...