Question 1 2 pts Consider a Bayesian classifier, where the model is a logistic regression. The probability distribution of the target variable Y, given the input features X;, Xo,..., Xn, is modelled as: P(Y 1|X 1, Xo,. . .,Xn,W) a(Wo + WX, + WeXeo +--+ W,Xn) where g is the sigmoid function and W = [Wo, W1,..., W,,] are the model parameters. In the Bayesian framework, the weights W are treated as random variables with a prior distribution. Which of the following statements best describes the posterior distribution in this model, given the observed data? The probability of observing the data given a specific set of weights W The updated belief about the weights W after observing the data. The prior belief about the weights W before observing any data. The probability of observing the data based on prior beliefs about the weights W