Maximum Likelihood, Logistic Regression, and Stochastic ...
The distributions may be either probability mass functions (pmfs) or probability density functions (pdfs). Suppose that we have a random sample drawn from a fixed but unknown member of this family. The random sample is a training set of nexamples x 1 to x n. An example may also be called an observation, an outcome, an instance, or a data point.
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Maximum Likelihood, Logistic Regression, and Stochastic ...
cseweb.ucsd.eduThe distributions may be either probability mass functions (pmfs) or probability density functions (pdfs). Suppose that we have a random sample drawn from a fixed but unknown member of this family. The random sample is a training set of nexamples x 1 to x n. An example may also be called an observation, an outcome, an instance, or a data point.