2,666,041 research outputs found
Towards Parameterized Regular Type Inference Using Set Constraints
We propose a method for inferring \emph{parameterized regular types} for
logic programs as solutions for systems of constraints over sets of finite
ground Herbrand terms (set constraint systems). Such parameterized regular
types generalize \emph{parametric} regular types by extending the scope of the
parameters in the type definitions so that such parameters can relate the types
of different predicates. We propose a number of enhancements to the procedure
for solving the constraint systems that improve the precision of the type
descriptions inferred. The resulting algorithm, together with a procedure to
establish a set constraint system from a logic program, yields a program
analysis that infers tighter safe approximations of the success types of the
program than previous comparable work, offering a new and useful efficiency vs.
precision trade-off. This is supported by experimental results, which show the
feasibility of our analysis
Incorporating Nuisance Parameters in Likelihoods for Multisource Spectra
We describe here the general mathematical approach to constructing
likelihoods for fitting observed spectra in one or more dimensions with
multiple sources, including the effects of systematic uncertainties represented
as nuisance parameters, when the likelihood is to be maximized with respect to
these parameters. We consider three types of nuisance parameters: simple
multiplicative factors, source spectra "morphing" parameters, and parameters
representing statistical uncertainties in the predicted source spectra.Comment: Presented at PHYSTAT 2011, CERN, Geneva, Switzerland, January 2011,
to be published in a CERN Yellow Repor
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