Editing Mixture model (section)

===House prices===
Assume that we observe the prices of ''N'' different houses.  Different types of houses in different neighborhoods will have vastly different prices, but the price of a particular type of house in a particular neighborhood (e.g., three-bedroom house in moderately upscale neighborhood) will tend to cluster fairly closely around the mean.  One possible model of such prices would be to assume that the prices are accurately described by a mixture model with ''K'' different components, each distributed as a [[normal distribution]] with unknown mean and variance, with each component specifying a particular combination of house type/neighborhood.  Fitting this model to observed prices, e.g., using the [[expectation-maximization algorithm]], would tend to cluster the prices according to house type/neighborhood and reveal the spread of prices in each type/neighborhood. (Note that for values such as prices or incomes that are guaranteed to be positive and which tend to grow [[exponential growth|exponentially]], a [[log-normal distribution]] might actually be a better model than a normal distribution.)