Signal-to-noise ratios in physical systems can be significantly degraded if the outputs of the systems are highly variable. Biological processes for which highly stereotyped signal generations are necessary features appear to have reduced their signal variabilities by employing multiple processing steps. To better understand why this multistep cascade structure might be desirable, we prove that...

multi agent markov decision processes (mmdps), as the generalization of markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for multi agent reinforcement learning. in this paper, a generalized learning automata based algorithm for finding optimal policies in mmdp is proposed. in the proposed algorithm, mmdp ...

in this paper at rst, a history of mathematical models is given.next, some basic information about random variables, stochastic processesand markov chains is introduced. as follows, the entropy for a discrete timemarkov process is mentioned. after that, the entropy for sis stochastic modelsis computed, and it is proved that an epidemic will be disappeared after a longtime.

1.1 Markov Processes Consider a set of states S1, S2, . . . SN . A discrete Markov process is one in which the system is in a particular state at any given time. The state can be changed only at discrete intervals of time. We denote the time instants associated with state changes with as t = 1, 2, . . . and we denote the actual state at time t as qt. The current state in an n-order Markov proce...