Review:
Metropolis Hastings Algorithm
overall review score: 4.2
⭐⭐⭐⭐⭐
score is between 0 and 5
The Metropolis-Hastings Algorithm is a Markov Chain Monte Carlo (MCMC) technique used for generating random samples from a probability distribution.
Key Features
- Markov Chain Monte Carlo technique
- Random sample generation
- Sampling from probability distribution
Pros
- Efficient sampling method for complex distributions
- Widely used in Bayesian statistics and machine learning
Cons
- Can be computationally expensive for large datasets
- Requires careful tuning of parameters