published: Oct. 12, 2011, recorded: September 2011, views: 27265
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Inference is the process of discovering from data about mechanisms that may have caused or generated that data, or at least explain it. The goals are varied - perhaps simply predicting future data, or more ambitiously drawing conclusions about scientific or societal truths. In the language of applied mathematics, these are inverse problems. Bayesian inference is about using probability to do all this. One of its strengths is that all sources of uncertainty in a problem can be simultaneously and coherently considered. It is model-based (in the language of machine learning, these are generative models), and we can use Bayesian methods to choose and criticize the models we use.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !