This work is aimed at extending the Poisson-Gamma models towards a more general specification, where the linear predictor of covariates is replaced by an additive predictor of generic functions of these covariates. Just like the generalized additive models (GAM), the linear functions of covariates are a particular case of additive models and the natural cubic splines are used as smoothing functions. The semiparametric specification allows to enlarge the possibilities of application of these models. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

This work is aimed at extending a class of state space models for Poisson count data, so called Poisson-Gamma models, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

This work is an extension of the state space model for
Poisson count data, Poisson-Gamma model, towards a
semiparametric specification. Just like the generalized
additive models (GAM), cubic splines are used for covariate
smoothing. The semiparametric models are fitted by an iterative
process that combines maximization of likelihood and
backfitting algorithm.

This work is an extension of the state space model for Poisson count data, Poisson-Gamma model, towards a semiparametric specification. Just like the generalized additive models (GAM), cubic splines are used for covariate smoothing. The semiparametric models are fitted by an iterative process that combines maximization of likelihood and backfitting algorithm.

Bayesian Additive Regression Trees Using Particle Gibbs Sampler
and Gibbs/Metropolis-Hastings Sampler
c(person("Pingyu","Wang", role = c("aut", "cre"),email = "applewangpingyu@gmail.com"),
person("Dai","Feng", role = "aut",email = "dai_feng@merck.com"),
person("Yang","Bai",  role = "aut",email = "by0132@live.cn"),
person("Qiuyue","Shi", role = "aut",email = "shiqiuyue@bupt.edu.cn"),
person("Zhicheng","Zhao", role = "aut",email = "zhaozc@bupt.edu.cn"),
person("Fei","Su", role = "aut",email = "sufei@bupt.edu.cn"),
person("Hugh","Chipman", role = "aut",email = "hugh.chipman@gmail.com"),
person("Robert","McCulloch", role = "aut",email = "robert.e.mcculloch@gmail.com"))
Pingyu Wang [aut, cre],
Dai Feng [aut],
Yang Bai [aut],
Qiuyue Shi [aut],
Zhicheng Zhao [aut],
Fei Su [aut],
Hugh Chipman [aut],
Robert McCulloch [aut]
The Particle Gibbs sampler and Gibbs/Metropolis-Hastings sampler were implemented
to fit Bayesian additive regression tree model. Construction of the model (training) and prediction
for a new data set (testing) can be separated. Our reference paper is:
Lakshminarayanan B, Roy D, Teh Y W. Particle Gibbs for Bayesian additive regression trees[C],
Artificial Intelligence and Statistics. 2015: 553-561,
<http://proceedings.mlr.press/v38/lakshminarayanan15.pdf>.