What is meant by Metropolis algorithm?
The Metropolis algorithm is a probabilistic algorithm used to generate samples from a probability distribution that is difficult to sample from directly. Originally developed in statistical mechanics, it is commonly used in statistical physics, Bayesian statistics, and machine learning. The algorithm allows for the exploration of states in a state space by using probabilities to accept state changes, even if these changes increase energy or cost, based on a temperature parameter.
Typical software functions in the area of "Metropolis algorithm":
- State Generation: Generation of new states based on the current distribution.
- Probability Calculation: Calculation of probabilities for accepting or rejecting a new state.
- Temperature Control: Adjustment of temperature parameters to influence the acceptance probability.
- Chain Construction: Implementation of Markov chains to generate samples.
- Result Documentation: Logging of generated states and their acceptance.
- Convergence Analysis: Checking the convergence of generated samples to the target distribution.
- Data Visualization: Graphical representation of results for analysis of the distribution and generated states.