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## how to use rating

- http://www.evanmiller.org/how-not-to-sort-by-average-rating.html
- http://www.evanmiller.org/bayesian-average-ratings.html
- http://www.evanmiller.org/ranking-items-with-star-ratings.html

## Four Assumptions Of Multiple Regression That Researchers Should Always Test

From:http://pareonline.net/getvn.asp?n=2&v=8

- VARIABLES ARE NORMALLY DISTRIBUTED.
- A LINEAR RELATIONSHIP BETWEEN THE INDEPENDENT AND DEPENDENT VARIABLE(S).
- VARIABLES ARE MEASURED WITHOUT ERROR (RELIABLY)
- HOMOSCEDASTICITY

## Naive Bayes classifier Probabilistic model

Abstractly, the probability model for a classifier is a conditional model.

over a dependent class variable C with a small number of outcomes or classes, conditional on several feature variables through . The problem is that if the number of features n is large or when a feature can take on a large number of values, then basing such a model on probability tables is infeasible. We therefore reformulate the model to make it more tractable.

## Probit Models — An Application Example

http://www.sts.uzh.ch/past/hs09/em/topic9a_p.pdf

## Bayesian inference

**Statistical inference**is the process of drawing conclusions from data that are subject to random variation.

Bayesian inference

Bayesian inference is a method of inference in which Bayes’ rule is used to update the probability estimate for a hypothesis as additional evidence is acquired. Bayesian updating is an important technique throughout statistics, and especially in mathematical statistics.