
Monte Carlo Inference via Greedy Importance Sampling
We present a new method for conducting Monte Carlo inference in graphica...
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A fast Monte Carlo test for preferential sampling
The preferential sampling of locations chosen to observe a spatiotempor...
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Modeling Sums of Exchangeable Binary Variables
We introduce a new model for sums of exchangeable binary random variable...
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Solving Dynamic Discrete Choice Models Using Smoothing and Sieve Methods
We propose to combine smoothing, simulations and sieve approximations to...
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CoGeneration with GANs using AIS based HMC
Inferring the most likely configuration for a subset of variables of a j...
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Fully probabilistic quasar continua predictions near Lymanα with conditional neural spline flows
Measurement of the red damping wing of neutral hydrogen in quasar spectr...
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A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for...
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Conditional Monte Carlo revisited
Conditional Monte Carlo refers to sampling from the conditional distribution of a random vector X given the value T(X) = t for a function T(X). Classical conditional Monte Carlo methods were designed for estimating conditional expectations of functions of X by sampling from unconditional distributions obtained by certain weighting schemes. The basic ingredients were the use of importance sampling and change of variables. In the present paper we reformulate the problem by introducing an artificial parametric model, representing the conditional distribution of X given T(X)=t within this new model. The key is to provide the parameter of the artificial model by a distribution. The approach is illustrated by several examples, which are particularly chosen to illustrate conditional sampling in cases where such sampling is not straightforward. A simulation study and an application to goodnessoffit testing of real data are also given.
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