Probabilistic methods are a branch of mathematics and statistics that involve the use of probability theory to model and analyze uncertain or random phenomena. These methods are used in a wide range of fields, including machine learning, artificial intelligence, economics, finance, and engineering. In probabilistic methods, the uncertainty or randomness in a system is represented using probability distributions. These distributions allow researchers to quantify the likelihood of different outcomes occurring and make informed decisions based on this information. Common techniques in probabilistic methods include Bayesian inference, Markov chains, Monte Carlo simulation, and stochastic processes. Overall, probabilistic methods provide a powerful framework for dealing with uncertainty and variability in both theoretical and practical applications. They are widely used in fields where there is inherent unpredictability or randomness in the data, allowing researchers to make more accurate predictions and decisions.