Review:
Gibbs Sampling
overall review score: 4.3
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score is between 0 and 5
Gibbs sampling is a Markov Chain Monte Carlo (MCMC) algorithm used for generating samples from complex probability distributions. It is commonly used in statistics, machine learning, and Bayesian inference.
Key Features
- Iterative algorithm
- Sampling from conditional distributions
- Convergence towards target distribution
Pros
- Efficient for sampling from high-dimensional distributions
- Relatively easy to implement
Cons
- Can be slow to converge for highly correlated variables
- Sensitive to initial conditions