Explore advanced techniques for scaling Bayesian inference in the context of big and complex data in this 47-minute lecture by David Dunson from Duke University. Delve into the computational challenges in machine learning, comparing traditional approaches with deep learning methods. Examine mixing rate problems, MCMC games, and strategies for handling large datasets. Learn about stochastic approximation, sparse linear programming, and theoretical guarantees in Bayesian inference. Investigate popular algorithms for logistic regression and Gaussian process models, understanding their limitations and potential improvements. Gain insights into why certain algorithms fail and consider innovative approaches to overcome these challenges in the field of machine learning and Bayesian statistics.