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As important is the question of designing optimal sampling policies that make it possible to build maps of high probability when the model is known.
In the field of spatial statistics, the design of sampling policies has been largely studied in the case of continuous variables, using tools from the geostatistics domain.
Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high.
Second, the differences in local sampling policies may affect the number and type of community-acquired MRSA identified.
Increased incidence of MRSA-positive cultures could be related to changes in sampling policies in this institution (e.g., more frequent sampling, surveillance cultures).
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning.
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In this work, we design the sampling policy in sampled-data systems.
Simulated annealing is often suggested as a possible sampling policy in combinatorial methods.
The problem of designing an adaptive sampling policy in an MRF can be formalized as an optimization problem.
Otherwise, with instances without labels, we discusses the sampling policy and various classifying loss expressions to simplifies and improves the classifiers.
As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses.
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