markovian Antonyms

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Meaning of markovian

Wordnet

markovian (a)

relating to or generated by a Markov process

markovian Sentence Examples

  1. The Markov chain is a stochastic process where the next state depends only on the current state, making it Markovian in nature.
  2. The Markovian model assumes that the system evolves randomly, with the probability of a particular transition depending only on the current state.
  3. In a Markovian decision process, the agent selects actions based on the current state, and the transition probabilities are Markovian.
  4. The Markov property simplifies the analysis of complex systems by assuming Markovian behavior, reducing the dimensionality of the problem.
  5. Markovian jump processes are used to model systems with discrete state transitions that occur at random intervals.
  6. Markov chains are particularly useful in modeling queuing systems, where the state of the system depends only on the current number of customers.
  7. Markovian arrival processes are used to describe the arrivals of events or customers according to a Markovian distribution.
  8. Markov blankets are sets of variables that shield a target variable from the rest of the system, allowing for efficient information processing.
  9. In reinforcement learning, Markovian environments are those where the agent's actions and rewards depend only on the current state, not the past history.
  10. The Markov assumption is widely used in natural language processing, assuming that the probability of a word appearing in a sentence depends only on the preceding words.

FAQs About the word markovian

relating to or generated by a Markov process

No synonyms found.

No antonyms found.

The Markov chain is a stochastic process where the next state depends only on the current state, making it Markovian in nature.

The Markovian model assumes that the system evolves randomly, with the probability of a particular transition depending only on the current state.

In a Markovian decision process, the agent selects actions based on the current state, and the transition probabilities are Markovian.

The Markov property simplifies the analysis of complex systems by assuming Markovian behavior, reducing the dimensionality of the problem.