Naive Bayes Theorem | Maximum A Posteriori Hypothesis | MAP Brute Force Algorithm by Mahesh Huddar

Naive Bayes Theorem | Maximum A Posteriori Hypothesis | MAP Brute Force Algorithm by Mahesh Huddar

Bayes theorem is the cornerstone of Bayesian learning methods because it provides a way to calculate the posterior probability P(h|D), from the prior probability P(h), together with P(D) and P(D(h).

The learner considers some set of candidate hypotheses H and is interested in finding the most probable hypothesis h ϵ H given the observed data D (or at least one of the maximally probable if there are several).

Machine Learning -    • Machine Learning  

Big Data Analysis -    • Big Data Analytics  

Data Science and Machine Learning - Machine Learning -    • Machine Learning  

Python Tutorial -    • Python Application Programming Tutorial  

naive bayes theorem in machine learning ,
naive bayes theorem,
naive bayes theorem in data mining,
naive bayes theorem probability,
naive bayes theorem in dwdm,
naive bayes theorem explained,
naive bayes rule example,
naive bayes rule,
maximum a posteriori estimation,
maximum a posteriori hypothesis,
maximum a posteriori (map) estimation,
maximum a posteriori vs maximum likelihood,
maximum a posteriori (map),
maximum a posteriori machine learning,
brute force map learning algorithm,
brute force map hypothesis,
brute force vs irradiance map