Discusses the Dirichlet distribution, Bayesian inference, posterior mean and variance, conjugate priors, and predictive distribution in the Dirichlet-Multinomial model.
Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Introduces Bayesian estimation, covering classical versus Bayesian inference, conjugate priors, MCMC methods, and practical examples like temperature estimation and choice modeling.
Explores Gaussian breakdown and return time distribution in chemistry, emphasizing the importance of forgetting constants and analyzing gene transcription factors.
Explores predictive consistency in sequential forecasting systems, emphasizing the utility of prediction over estimation and the significance of prequential approaches.
