E_alpha

base parameter of the tree prior, $alpha \in [0,1)$.

alpha


Expected value of the number of bottom nodes in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) and $\beta=0$ (Case #1).


Provides a way to simulate from the prior distribution of Bayesian trees by Chipman et al. (1998) <DOI:10.2307/2669832>. The prior distribution of Bayesian trees is highly dependent on the design matrix X, therefore using the suggested hyperparameters by Chipman et al. (1998) <DOI:10.2307/2669832> is not recommended and could lead to unexpected prior distribution. This work is part of my master thesis (expected 2016).

Alexia Jolicoeur-Martineau

BayesTreePrior

Bayesian Tree Prior Simulation

E_alpha function

Expected value of the number of bottom nodes in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) and $\beta=0$ (Case #1).

E_alpha: Expected value of the number of bottom nodes in the unrealistic case where we assume that the number of variables and possible splits are infinite (therefore P(T) is not dependent on the design matrix X) and $\beta=0$ (Case #1).

Description

Usage

Arguments

Value

References

See Also

Examples