Parallel tempering is a Monte Carlo method that uses multiple Markov chains at different temperatures to improve the sampling of complex probability distributions. By running several chains simultaneously, each at a different temperature, the technique allows for better exploration of the state space and helps to overcome local optima that can occur in traditional sampling methods. This approach enhances the efficiency of sampling, particularly for high-dimensional problems.
congrats on reading the definition of parallel tempering. now let's actually learn it.