BNN (version 1.0.2)

BNNprior: Prior Probability of Bayesian Neural Networks

Description

Calculating the prior probability of linear and nonlinear classes of BNN models.

Usage

BNNprior(dimX, dimY, hid_num = 3,lambda=0.025,total_iteration=1000000,popN = 20)

Arguments

dimX

Dimension of the input data.

dimY

The dimension of reponse data. It is restricted to 1 in the current version of the package.

hid_num

Number of hidden units. The default setting is 3.

lambda

The prior probability for each connection of the neural network being selected for the final model. The default setting is 0.025.

total_iteration

Number of total iterations, default of 1000,000.

popN

Number of Markov Chains, default of 20.

Value

prob

Prior probability assigned to the class of linear models.

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References

Liang, F., Li, Q., and Zhou, L. (2017). Bayesian Neural Networks for Selection of Drug Sensitive Genes. Journal of the American Statistical Association.

Examples

# NOT RUN {
library(BNN)
BNNprior(50, 1, hid_num = 3, lambda=0.025, total_iteration = 10000, popN = 5)
# }
# NOT RUN {
# }