Bayesian Learning. Pt 2. 6.7- 6.12 Machine Learning Promethea Pythaitha. Bayes Optimal Classifier. Gibbs Algorithm. Naïve Bayes Classifier. Bayesian Belief networks. EM algorithm. Bayesian Optimal classifier. So far we have asked: Which is the most likely-to be correct hypothesis:
Bayesian Learning. Pt 2. 6.7- 6.12 Machine LearningPromethea Pythaitha.
Any system that classifies instances using this system is a “Bayes Optimal Classifier”
Neural-nets or Decision-trees.
P(a1, a2, …, an| vj )*P(vj )
The Neutron star is a tiny
speck at the center of that
<Average, Medium> we could certainly put it’s mass into a stellar model that has been fine-tuned by our Neural Net, or, we could simply use a Bayesian Classification:
(true–mean 1, true-mean 2)
We cannot see the actual distributions.
In other words, the probability of getting a specific data point drops drastically as we go away from the mean.
Then the mean μ1 should be more to the left.
assuming zij = E(zij) as calculated using h.