Introduction
Embarking on the captivating journey of Bayesian inference where uncertainty transforms into knowledge, demands a nuanced understanding of computations.
Step 1 — The Essence of Bayesian Inference
At its core, Bayesian inference is about updating beliefs in the face of new evidence. Let’s dive into the heart of Bayesianism by exploring the Bayesian update formula.
Step 2 — The Power of Priors
Priors serve as the foundation of Bayesian analysis, encapsulating our beliefs before observing new data. Let’s choose a prior distribution and witness its transformation.
Step 3 — Sampling from the Posterior
Markov Chain Monte Carlo (MCMC) methods allow us to sample from complex posterior distributions. Here, we will use the Metropolis hastings algorithm for simplicity.
Step 4 — Visualizing the Results
Visualization is the key to understanding Bayesian computations. Let’s create a compelling visualization of our posterior distribution.
Conclusion
As we conclude this odyssey into Bayesian computations, remember that every line of code is a step toward demystifying uncertainty. Bayesian inference empowers us to embrace ambiguity and extract valuable insights from the chaos of data.
