Fixing Bayesian tools
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@ -98,7 +98,7 @@ res.print(out);
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//sp.setCaching(true);
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//sp.setCaching(true);
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//sp.setSuppressWarnings(true);
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//sp.setSuppressWarnings(true);
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//
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//
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//BayesianManager bm = new BayesianManager();
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//BayesianConfidenceLimit bm = new BayesianConfidenceLimit();
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//bm.printMarginalLikelihood(onComplete, "U2", res, ["E0", "N", "bkg", "U2", "X"], 10000);
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//bm.printMarginalLikelihood(onComplete, "U2", res, ["E0", "N", "bkg", "U2", "X"], 10000);
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// PrintNamed.printLike2D(Out.onComplete, "like", res, "N", "E0", 30, 60, 2);
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// PrintNamed.printLike2D(Out.onComplete, "like", res, "N", "E0", 30, 60, 2);
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@ -20,7 +20,7 @@ import hep.dataforge.data.DataSet
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import hep.dataforge.stat.fit.FitManager
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import hep.dataforge.stat.fit.FitManager
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import hep.dataforge.stat.fit.FitState
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import hep.dataforge.stat.fit.FitState
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import hep.dataforge.stat.fit.ParamSet
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import hep.dataforge.stat.fit.ParamSet
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import hep.dataforge.stat.likelihood.BayesianManager
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import hep.dataforge.stat.likelihood.BayesianConfidenceLimit
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import hep.dataforge.stat.models.XYModel
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import hep.dataforge.stat.models.XYModel
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import hep.dataforge.tables.ListTable
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import hep.dataforge.tables.ListTable
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import inr.numass.data.SpectrumGenerator
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import inr.numass.data.SpectrumGenerator
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@ -104,7 +104,7 @@ res.print(out);
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beta.setCaching(true);
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beta.setCaching(true);
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beta.setSuppressWarnings(true);
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beta.setSuppressWarnings(true);
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BayesianManager bm = new BayesianManager();
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BayesianConfidenceLimit bm = new BayesianConfidenceLimit();
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// bm.setPriorProb(new OneSidedUniformPrior("trap", 0, true));
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// bm.setPriorProb(new OneSidedUniformPrior("trap", 0, true));
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// bm.setPriorProb(new GaussianPrior("trap", 1d, 0.002));
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// bm.setPriorProb(new GaussianPrior("trap", 1d, 0.002));
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// bm.printMarginalLikelihood(Out.onComplete,"U2", res);
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// bm.printMarginalLikelihood(Out.onComplete,"U2", res);
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