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Distribution summary statistics of Bayesian linear regression model for predictor variable selection

To obtain a summary of a standard Bayesian linear regression model, see `summarize`

.

`summarize(Mdl)`

`SummaryStatistics = summarize(Mdl)`

`summarize(`

displays a tabular summary of
the random regression coefficients and disturbance variance of the Bayesian linear regression model
`Mdl`

)`Mdl`

at the command line. For each parameter, the summary includes the:

Standard deviation (square root of the variance)

95% equitailed credible intervals

Probability that the parameter is greater than 0

Description of the distributions, if known

Marginal probability that a coefficient should be included in the model, for stochastic search variable selection (SSVS) predictor-variable-selection models

returns a structure array with a table summarizing the regression coefficients and
disturbance variance, and a description of the joint distribution of the
parameters.`SummaryStatistics`

= summarize(`Mdl`

)

If

`Mdl`

is a`lassoblm`

model object and`Mdl.Probability`

is a numeric vector, then the 95% credible intervals on the regression coefficients are`Mean + [–2 2]*Std`

, where`Mean`

and`Std`

are variables in the summary table.If

`Mdl`

is a`mixconjugateblm`

or`mixsemiconjugateblm`

model object, then the 95% credible intervals on the regression coefficients are estimated from the mixture cdf. If the estimation fails, then`summarize`

returns`NaN`

values instead.