
Optimal Monte Carlo Estimation of Belief Network Inference
We present two Monte Carlo sampling algorithms for probabilistic inferen...
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Statistical errors in Monte Carlobased inference for random elements
Monte Carlo simulation is useful to compute or estimate expected functio...
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Monte Carlo Fusion
This paper proposes a new theory and methodology to tackle the problem o...
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Motor Unit Number Estimation via Sequential Monte Carlo
A change in the number of motor units that operate a particular muscle i...
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Monte Carlo modified profile likelihood in models for clustered data
The main focus of the analysts who deal with clustered data is usually n...
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Sequential Monte Carlo Bandits
In this paper we propose a flexible and efficient framework for handling...
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Probabilistic treatment of the uncertainty from the finite size of weighted Monte Carlo data
The finite size of Monte Carlo samples carries intrinsic uncertainty tha...
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Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality
Statisticians are usually glad to obtain additional data, but Monte Carlo inference can lead to an embarrassment of riches since the appealing generality of Monte Carlo methods can come at the expense of poor scalability. We consider statistically efficient simulationbased likelihood inference with a computational budget of size essentially n^3/2 for a dataset of size n. This methodology takes advantage of asymptotic properties of the likelihood function in an n^1/4 neighborhood of the true parameter value. We obtain a rescaled version of local asymptotic normality, and on this scale we show that statistically efficient estimation is possible despite inconsistent Monte Carlo likelihood evaluation.
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