We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones. We demonstrate that semiparametric ...
Wikle, C., Berliner, L., and Cressie, N. (1998), “Hierarchical Bayesian Space-Time Models,”Environmental and Ecological Statistics, 5, 117–154. ArticleGoogle Scholar Wikle, C., and Anderson, C. (2003), “Climatological Analysis of Tornado Report Counts Using a Hierarchical Bayesian Spatio...
Our paper, on the other hand, focuses on the Bayesian Nash Equilibrium (BNE) concept (see, e.g., Fudenberg and Tirole, 1991), which delivers a different set of structural equations from Aradillas-Lopez (2010)’s. We provide a more detailed comparison in Section 6.1. When the BNE is a...
In the classical Gaussian Bayesian networks, the regression function between nodes has always been assumed to be linear. Actually, this is not necessary because the links between nodes may be more complex than simply linear relationships. Learning the structure of the semiparametric Bayesian network, ...
Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation ...
Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes, facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory...
e. Mu¨ller, and D. e. Sinha (1998). Practical Nonparametric and Semiparametric Bayesian Statistics. Springer-Verlag Inc.Rios Insua, D. and Mu¨ller, P. 1998. Feedforward neural networks for non- parametric regression. In Practical Nonparametric and Semiparametric Bayesian Statistics, ed. D. ...
COVID-19Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model...
Texas A&M University.Lin, Zhongjian.Texas A&M University.Rossi, P. E. (2014). Bayesian Non- and Semiparametric Methods and Applications. Princeton: Princeton University Press.
Bayesian networksSemiparametric modelKernel density estimationHybrid dataThis paper presents a new class of Bayesian networks called hybrid semiparametric Bayesian networks, which can model hybrid data (discrete and continuous data) by mixing parametric and nonparametric estimation models. The parametric ...