Difference between revisions of "Probabilistic likelihood model"
(New page: In simple terms a probabilistic likelihood model is a mathematical model which gives the probability of some observation (data) for some stated mathematical model capable of predicting...) |
Moronicorbit (talk | contribs) (add category) |
||
| Line 1: | Line 1: | ||
In simple terms a probabilistic likelihood model is a [[mathematical model]] which gives the probability of some observation (data) for some stated mathematical model capable of predicting said data as an outcome (observation). It is typical denoted as ''p''(''D''|''M'') which is simply the conditional probability for the data ''D'' given some model ''M''. In [[parameter estimation]] it often written as ''p''(''D''|''a'') where ''a'' is a [[mathematical parameter]] of the model class M which would then indicate a family of associated models which only differ by the value of the parameter, e.g., a family of models which give the likelihood (probability) of observing a coin flip of heads, where the parameter ''a'' would indicate different possible weightings of a coin which would change the odds of it landing on heads in an unbiased flip. | In simple terms a probabilistic likelihood model is a [[mathematical model]] which gives the probability of some observation (data) for some stated mathematical model capable of predicting said data as an outcome (observation). It is typical denoted as ''p''(''D''|''M'') which is simply the conditional probability for the data ''D'' given some model ''M''. In [[parameter estimation]] it often written as ''p''(''D''|''a'') where ''a'' is a [[mathematical parameter]] of the model class M which would then indicate a family of associated models which only differ by the value of the parameter, e.g., a family of models which give the likelihood (probability) of observing a coin flip of heads, where the parameter ''a'' would indicate different possible weightings of a coin which would change the odds of it landing on heads in an unbiased flip. | ||
| + | |||
| + | [[Category:Mathematics]] | ||
Revision as of 19:52, December 8, 2007
In simple terms a probabilistic likelihood model is a mathematical model which gives the probability of some observation (data) for some stated mathematical model capable of predicting said data as an outcome (observation). It is typical denoted as p(D|M) which is simply the conditional probability for the data D given some model M. In parameter estimation it often written as p(D|a) where a is a mathematical parameter of the model class M which would then indicate a family of associated models which only differ by the value of the parameter, e.g., a family of models which give the likelihood (probability) of observing a coin flip of heads, where the parameter a would indicate different possible weightings of a coin which would change the odds of it landing on heads in an unbiased flip.