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Many common inference problems in computational genetics depend on inferring aspects of the evolutionary history of a data set given a set of observed modern sequences.
An innocent puzzle requiring the traverse of a path may lead to technicalities of graph theory; a simple problem of counting parts of a geometric figure may involve combinatorial theory; dissecting a polygon may involve transformation geometry and group theory; logical inference problems may involve matrices.
Programs of this type obviously can not solve hard inference problems.
Scatter matrix estimation and hypothesis testing are fundamental inference problems in a wide variety of signal processing applications.
It shows that including such restrictions in the description language leaves the important inference problems such as instance testing decidable.
However, because the size and complexity of inference problems has dramatically increased, improved MCMC methods are required.
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b Output graph of source inference problem.
First, the #P-completeness of source inference problem is proven.
We considered cascade source inference problem in the IC model.
We prove that the type inference problem is NP-complete.
The following theorem shows the intractability of source inference problem, i.e., solving (2) given G, τ, and A τ. Source inference problem is #P-complete.
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