> ML_ALGORITHM // MARKOV-CHAIN-MONTE-CARLO-METROPOLIS-HASTINGS_v1.0
Markov Chain Monte Carlo (MCMC / Metropolis-Hastings)
Foundational MCMC algorithm that samples from complex, unnormalized target probability distributions by constructing a Markov chain with the desired equilibrium distribution.
Markov Chain Monte Carlo Samplingbayesian-probabilistichigh-intrinsicsmall (<1k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(chains * samples * likelihood_eval)
Inference Complexity:O(samples) empirical posterior summaries
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:low
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:small (<1k)
Interpretability Assessment
Generates full empirical posterior probability distributions and credible intervals for every parameter.
Suitable Tasks & Supported Modalities
Suitable Tasks:
bayesian inferenceposterior parameter estimation
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
Common Pitfalls & Warnings
- Random-walk proposals get stuck in high dimensions (>20 params), resulting in abysmal acceptance rates and poor mixing (use HMC instead)