Demystifying Bayesian Model Fitting in Deep Learning

<h1>Introduction to Bayesian Inference</h1> Bayesian inference serves as the theoretical backbone of Bayesian Model Fitting, grounded in the concept of updating our beliefs in the light of new evidence. Central to this process is the prior distribution, p(θ∣H), which encapsulates our initial beliefs about the model parameters, θ, based on hypothesis H. This probabilistic framework not only allows for an intuitive understanding of model parameters but also sets the stage for a systematic update mechanism through observed data. <a href="https://medium.com/@chandra.pcs/demystifying-bayesian-model-fitting-in-deep-learning-34eb9418cec6">Visit Now</a>