Hello,
Thank you for your contributions, which is a great help for me to understanding the BART model. But I found something questionable about the calculation of the likelihood ratio when sampling to modify the tree structure. Please take a look at the function log_grow_ratio in the file bartpy/samplers/unconstrainedtree/likelihoodratio.py, line 20
I think there are something wrong with n * sigma_mu. It should be corrected as
first_term = (var * (var + n * var_mu)) / ((var + n_l * var_mu) * (var + n_r * var_mu))
Same question is also found in the log_grow_function in the file bartpy/samplers/oblivioustrees/likelihoodratio.py
My thoughts are based on the paper bartMachine: Machine Learning with Bayesian Additive Regression Tree, A.1. part. You will be greatly appreciated to point that out if I am wrong. Hope my thoughts be helpful to you.
Hello, Thank you for your contributions, which is a great help for me to understanding the BART model. But I found something questionable about the calculation of the likelihood ratio when sampling to modify the tree structure. Please take a look at the function
log_grow_ratioin the filebartpy/samplers/unconstrainedtree/likelihoodratio.py, line 20first_term = (var * (var + n * sigma_mu)) / ((var + n_l * var_mu) * (var + n_r * var_mu))I think there are something wrong with
n * sigma_mu. It should be corrected asfirst_term = (var * (var + n * var_mu)) / ((var + n_l * var_mu) * (var + n_r * var_mu))Same question is also found in the
log_grow_functionin the filebartpy/samplers/oblivioustrees/likelihoodratio.pyMy thoughts are based on the paper
bartMachine: Machine Learning with Bayesian Additive Regression Tree, A.1. part. You will be greatly appreciated to point that out if I am wrong. Hope my thoughts be helpful to you.