2025年3月11日 · A stochastic process is called Markovian (after the Russian mathematician Andrey Andreyevich Markov) if at any time t the conditional probability of an arbitrary future event given the entire past of the process—i.e., given X (s) for all s ≤ t —equals the conditional probability of that future event given only X (t).

The meaning of MARKOVIAN is of, relating to, or resembling a Markov process or Markov chain especially by having probabilities defined in terms of transition from the possible existing states to other states.

2015年7月1日 · Everything is Markovian. Really all the physics (and chemistry and biology) most of us are interested in, in fact, is Markovian or effectively so. Once the states and velocities/momenta of all particles are accounted for, the future (or distribution of …

A Markov Decision Process (MDP) model contains: • A set of possible world states S • A set of possible actions A • A real valued reward function R(s,a) • A description Tof each action’s effects in each state. We assume the Markov Property: the effects of an action taken in a state depend only on that state and not on the prior history.

theory (Markovian sources), linguistics (Markovian models of language production), speech recognition, internet search (Google’s Pagerank algorithm is based upon a Markovian model of a random surfer).

A fundamental result in the theory of Markov Decision processes is that the optimal policy is Markovian and the optimal value functions satisfy an elegant recursive de nition. This is captured in the following theorem.

2015年1月26日 · In Image Processing context, images are space-varying signals instead (well, as long as you don't have video). Hence, the Markovian assumption applied to images means that the value/class of a pixel (spatial location) depends only on the values of its close (spatial) neighbours (for example the pixels directly North, South East, West). $\endgroup$

A Markovian process is defined as a system where transitions between discrete states occur with fixed probabilities, and the transition probabilities depend only on the previous state, making it memoryless.

Markovian Decision Process Chapter Guide. This chapter applies dynamic programming to the solution of a stochas-tic decision process with a finite number of states.The transition...

2024年12月13日 · The Markov chain is a stochastic process in which the future value of a variable is conditionally independent of the past, given its present value. Data with Markovian features are characterized by: ...