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Probabilistic Bisimulation
In theoretical computer science, probabilistic bisimulation is an extension of the concept of bisimulation for fully probabilistic transition systems first described by K.G. Larsen and A. Skou. A discrete probabilistic transition system is a triple : S = (\operatorname, \operatorname, \tau:\operatorname \times \operatorname\times \operatorname\rightarrow ,1 where \tau(s,a,t) gives the probability of starting in the state ''s'', performing the action ''a'' and ending up in the state ''t''. The set of states is assumed to be countable. There is no attempt to assign probabilities to actions. It is assumed that the actions are chosen nondeterministically by an adversary or by the environment. This type of system is fully probabilistic, there is no other indeterminacy. The definition of a probabilistic bisimulation on a system ''S'' is an equivalence relation ''R'' on the state space St, such that for every pair ''s'',''t'' in St with sRt and for every action a in Act and for ev ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Theoretical Computer Science
Theoretical computer science (TCS) is a subset of general computer science and mathematics that focuses on mathematical aspects of computer science such as the theory of computation, lambda calculus, and type theory. It is difficult to circumscribe the theoretical areas precisely. The Association for Computing Machinery, ACM's ACM SIGACT, Special Interest Group on Algorithms and Computation Theory (SIGACT) provides the following description: History While logical inference and mathematical proof had existed previously, in 1931 Kurt Gödel proved with his incompleteness theorem that there are fundamental limitations on what statements could be proved or disproved. Information theory was added to the field with a 1948 mathematical theory of communication by Claude Shannon. In the same decade, Donald Hebb introduced a mathematical model of Hebbian learning, learning in the brain. With mounting biological data supporting this hypothesis with some modification, the fields of n ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bisimulation
In theoretical computer science a bisimulation is a binary relation between state transition systems, associating systems that behave in the same way in that one system simulates the other and vice versa. Intuitively two systems are bisimilar if they, assuming we view them as playing a ''game'' according to some rules, match each other's moves. In this sense, each of the systems cannot be distinguished from the other by an observer. Formal definition Given a labelled state transition system (S, \Lambda, →), where S is a set of states, \Lambda is a set of labels and → is a set of labelled transitions (i.e., a subset of S \times \Lambda \times S), a bisimulation is a binary relation R \subseteq S \times S, such that both R and its converse R^T are simulations. From this follows that the symmetric closure of a bisimulation is a bisimulation, and that each symmetric simulation is a bisimulation. Thus some authors define bisimulation as a symmetric simulation. Equivalentl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
State Transition System
In theoretical computer science, a transition system is a concept used in the study of computation. It is used to describe the potential behavior of discrete systems. It consists of states and transitions between states, which may be labeled with labels chosen from a set; the same label may appear on more than one transition. If the label set is a singleton, the system is essentially unlabeled, and a simpler definition that omits the labels is possible. Transition systems coincide mathematically with abstract rewriting systems (as explained further in this article) and directed graphs. They differ from finite-state automata in several ways: * The set of states is not necessarily finite, or even countable. * The set of transitions is not necessarily finite, or even countable. * No "start" state or "final" states are given. Transition systems can be represented as directed graphs. Formal definition Formally, a transition system is a pair (S, \rightarrow) where S is a set of st ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Chains
A Markov chain or Markov process is a stochastic process, stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs ''now''." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC). It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, such as studying cruise control, cruise control systems in motor vehicles, queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo, which are used for simulating sampl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lumpability
In probability theory, lumpability is a method for reducing the size of the state space of some continuous-time Markov chains, first published by Kemeny and Snell. Definition Suppose that the complete state-space of a Markov chain is divided into disjoint subsets of states, where these subsets are denoted by ''ti''. This forms a partition \scriptstyle of the states. Both the state-space and the collection of subsets may be either finite or countably infinite. A continuous-time Markov chain \ is lumpable with respect to the partition ''T'' if and only if, for any subsets ''ti'' and ''tj'' in the partition, and for any states ''n,n’'' in subset ''ti'', : \sum_ q(n,m) = \sum_ q(n',m) , where ''q''(''i,j'') is the transition rate from state ''i'' to state ''j''. Similarly, for a stochastic matrix ''P'', ''P'' is a lumpable matrix on a partition ''T'' if and only if, for any subsets ''ti'' and ''tj'' in the partition, and for any states ''n,n’'' in subset ''ti'', : \sum_ p(n,m) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
