MCMC uncertainty estimation applies Bayesian statistics and Markov chain Monte Carlo (MCMC) methods to estimate the uncertainty associated with model parameters in various fields. It enables researchers to quantify uncertainty, incorporate prior information, and perform complex statistical analyses to make more informed decisions. MCMC is used in healthcare for disease modeling and diagnosis, in environmental science for climate change projections, and in machine learning for optimizing algorithms and interpreting complex data.
