{"id":16665,"date":"2020-01-19T23:25:57","date_gmt":"2020-01-20T04:25:57","guid":{"rendered":"https:\/\/bangla.salearningschool.com\/recent-posts\/?p=16665"},"modified":"2020-02-08T09:41:54","modified_gmt":"2020-02-08T14:41:54","slug":"bayesian-statistics-and-machine-learning","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=16665","title":{"rendered":"Bayesian Statistics and Machine Learning"},"content":{"rendered":"

Bayesian Statistics and Machine Learning
\n<\/strong>
\n“Bayesian inference<\/strong> is a method of statistical inference<\/strong> in which Bayes<\/strong>‘ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference<\/strong> is an important technique in statistics, and especially in mathematical statistics.”<\/p>\n

en.wikipedia.org \u203a wiki \u203a Bayesian_inference
\n<\/a><\/p>\n

Bayesian inference – Wikipedia<\/a><\/h3>\n

“Firstly, (statistical) inference is the process of deducing properties about a population or probability distribution from data”<\/p>\n

“Bayesian inference is therefore just the process of deducing properties about a population or probability distribution from data using Bayes\u2019 theorem.<\/em> That\u2019s it.”
\nhttps:\/\/towardsdatascience.com\/probability-concepts-explained-bayesian-inference-for-parameter-estimation-90e8930e5348<\/a><\/p>\n

Introduction to Bayesian Inference<\/h1>\n

https:\/\/blogs.oracle.com\/datascience\/introduction-to-bayesian-inference<\/a><\/p>\n

”<\/p>\n

Bayesian Linear Regression<\/h1>\n

In the Bayesian viewpoint, we formulate linear regression using probability distributions rather than point estimates. The response, y, is not estimated as a single value, but is assumed to be drawn from a probability distribution. The model for Bayesian Linear Regression with the response sampled from a normal distribution is:”<\/p>\n\n\t\t