Introduction to Bayesian Inference
Explore Bayesian inference through quantum Bayesian networks, learning to estimate probabilities and predict survival outcomes by applying Bayes' Theorem and performing marginal, posterior, and MAP inference with hidden variables.
We'll cover the following...
We’ve implemented our quantum Bayesian network. It represents a passenger’s overall chance to survive the Titanic shipwreck. It considers two features, the passenger’s sex and whether the passenger was a child.
It’s time to use this network. We want to infer something we don’t already know. We perform inference.
Generally, statistical inference is the process of deducing properties about a population or probability distribution from data. This is why we build the entire network. We want to be able to make predictions about some new data from the data we already know.
Specifically, Bayesian inference is the process of deducing properties about a ...