Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Stochastic Model in Bayesian Inference: Theoretical
  Implications and Efficient Approximation   We classify two types of Hierarchical Bayesian Model found in the literature
as Hierarchical Prior Model (HPM) and Hierarchical Stochastic Model (HSM).
Then, we focus on studying the theoretical implications of the HSM. Using
examples of polynomial functions, we show that the HSM is capable of separating
different types of uncertainties in a system and quantifying uncertainty of
reduced order models under the Bayesian model class selection framework. To
tackle the huge computational cost for analyzing HSM, we propose an efficient
approximation scheme based on Importance Sampling and Empirical Interpolation
Method. We illustrate our method using two examples - a Molecular Dynamics
simulation for Krypton and a pharmacokinetic/pharmacodynamic model for cancer
drug.

19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-fin