Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying the Uncertainty in Model Parameters Using Gaussian
  Process-Based Markov Chain Monte Carlo: An Application to Cardiac
  Electrophysiological Models   Estimation of patient-specific model parameters is important for personalized
modeling, although sparse and noisy clinical data can introduce significant
uncertainty in the estimated parameter values. This importance source of
uncertainty, if left unquantified, will lead to unknown variability in model
outputs that hinder their reliable adoptions. Probabilistic estimation model
parameters, however, remains an unresolved challenge because standard Markov
Chain Monte Carlo sampling requires repeated model simulations that are
computationally infeasible. A common solution is to replace the simulation
model with a computationally-efficient surrogate for a faster sampling.
However, by sampling from an approximation of the exact posterior probability
density function (pdf) of the parameters, the efficiency is gained at the
expense of sampling accuracy. In this paper, we address this issue by
integrating surrogate modeling into Metropolis Hasting (MH) sampling of the
exact posterior pdfs to improve its acceptance rate. It is done by first
quickly constructing a Gaussian process (GP) surrogate of the exact posterior
pdfs using deterministic optimization. This efficient surrogate is then used to
modify commonly-used proposal distributions in MH sampling such that only
proposals accepted by the surrogate will be tested by the exact posterior pdf
for acceptance/rejection, reducing unnecessary model simulations at unlikely
candidates. Synthetic and real-data experiments using the presented method show
a significant gain in computational efficiency without compromising the
accuracy. In addition, insights into the non-identifiability and heterogeneity
of tissue properties can be gained from the obtained posterior distributions.

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The categories of this paper are: supr-con
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
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The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga