Bayesian methods
Bayesian methods provide a powerful and flexible framework for reasoning under uncertainty, allowing for the integration of prior knowledge and iterative learning as new evidence emerges.
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Common techniques used in Bayesian analysis include Bayesian inference, Markov Chain Monte Carlo (MCMC) methods, variational inference and Bayesian networks. These techniques have applications in a wide range of fields, such as medical diagnosis, climate modeling, natural language processing and finance. Bayesian methods have also been successfully applied to various machine learning tasks, offering advantages such as probabilistic modeling, uncertainty quantification and robustness to overfitting. Some common Bayesian methods used in machine learning are Bayesian regression, Bayesian neural networks, Gaussian processes, Markov Chain Monte Carlo (MCMC) methods, variational inference, Bayesian deep learning, Bayesian decision theory and Bayesian optimization.