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Model reduction with factored splits (MRFS) is an algorithm to compute a homogeneous partition for factored MDPs [6].
Based on these conditions, we can say that (mathcal {P} = {B_{1}, B_{2}}) is a homogeneous partition that results in an MDP M ′ containing only two abstract states (Fig. 8 (lower)).
Nonetheless, if the criteria (i)–(iii) above a homogeneous partition with correct probability values for each cell in the partition are satisfied, we may use this information to explain why $x$, who had a non-resistant strain of strep and was treated, recovered quickly.
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Finally, we compare the results from our algorithms which search in the space of non-homogeneous partitions with exact and approximate algorithms which represent homogeneous and approximately homogeneous partitions as decision trees or algebraic decision diagrams.
Since homogeneous partitions that define equivalent reduced-state-space MDPs can have a large number of blocks, we relax the requirement of homogeneity.
The proposed testability can also be used to demonstrate whether the test cases based on uniform partitions, such as branch coverage criteria, result in homogeneous partitions that is known to be more effective than random testing.
Splittings at the nodes are chosen to yield ever more homogeneous partitions and to minimise the entropy.
The algorithm constructs approximate aggregate MDPs from non-homogeneous partitions, solves the aggregate MDPs exactly, and then uses the resulting value functions as part of a heuristic in refining the current best non-homogeneous partition.
We present an algorithm for aggregating states in solving large MDPs (represented as factored MDPs) using search by successive refinement in the space of non-homogeneous partitions.
All partitioned homogeneous clusters of the representative subject were shown in a slice view, with randomized pigmentation for different clusters (Figure S3).
However, these models considered the lung as a single homogeneous compartment and partitioning between lung and blood was empirically defined based on in vivo measurements.
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CEO of Professional Science Editing for Scientists @ prosciediting.com