Markov Chain Monte Carlo
Artificial Intelligence
Mathematical systems that transition between states according to probabilistic rules where future states depend only on the current state.
Detailed Explanation
Markov Chain Models are mathematical frameworks used to predict a sequence of possible events where the next state depends solely on the current state, not past states. They utilize probabilistic rules to transition between states, making them useful in various AI applications such as natural language processing, speech recognition, and modeling stochastic processes. Their main characteristic is the memoryless property.
Use Cases
- •Predicting user behavior sequences in chatbots by modeling next states based solely on current user input.
Related Terms
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