You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

An example of such an outcome would be something ... becomes more dynamic with complex decisions, Bayesian probability models must be implemented to determine priori probabilities.

We built a statistical model that calculates the odds of a Black ... Also, as many parties are keen to have more diversity we assume a high probability that you will be pushed forward if you ...

A prediction model from the global financial services firm JPMorgan Chase, analyzed by Bloomberg, suggests that the probability of an economic downturn has grown to 31 percent from 17 percent at ...

Missing values can occur, for example ... between the steps of guessing a probability distribution over completions of missing data given the current model (known as the E-step) and then re ...