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/dports/science/dakota/dakota-6.13.0-release-public.src-UI/docs/KeywordMetadata/
H A Dmethod-bayes_calibration-calibrate_error_multipliers1 Blurb:: Calibrate hyper-parameter multipliers on the observation error covariance
4 observation error covariance (\ref
6 include \c one multiplier on the whole block-diagonal covariance
7 structure, one multiplier \c per_experiment covariance block, one
8 multiplier \c per_response covariance block, or separate multipliers
/dports/math/R-cran-RHmm/RHmm/man/
H A DsetAsymptoticCovMat.rd3 \title{Set the asymptotic covariance matrix of a fitted HMM}
4 \description{This function sets the empirical asymptotic covariance matrix of the fitted HMM}
10 \item{asymptCovMat}{The covariance matrix of the fitted model}
/dports/graphics/tesseract/tesseract-5.0.0/src/ccstruct/
H A Dquadlsq.cpp121 long double covariance = in fit() local
125 top96 = cubevar * covariance; in fit()
134 top96 = covariance - cubevar * a; in fit()
139 b = covariance / x_variance; in fit()
/dports/science/py-scipy/scipy-1.7.1/scipy/stats/
H A Dkde.py241 output_dtype = np.common_type(self.covariance, points)
293 sum_cov = self.covariance + cov
336 stdev = ravel(sqrt(self.covariance))[0]
371 self.covariance, **extra_kwds)
411 sum_cov = small.covariance + large.covariance
459 zeros((self.d,), float), self.covariance, size=size
568 self.covariance = self._data_covariance * self.factor**2
570 L = linalg.cholesky(self.covariance*2*pi)
/dports/math/openturns/openturns-1.18/python/test/
H A Dt_KrigingAlgorithm_std_hmat.py45 covariance = result.getConditionalCovariance(X)
46 covariancePoint = ot.Point(covariance.getImplementation())
48 ott.assert_almost_equal(covariance,
105 covariance = result.getConditionalCovariance(inputSample)
107 covariance, ot.SquareMatrix(len(inputSample)), 0.0, 1e-3)
/dports/math/openturns/openturns-1.18/python/src/
H A DSquaredExponential_doc.i.in2 "Squared exponential covariance function.
27 The *squared exponential function* is a stationary covariance function with dimension :math:`d=1`.
50 Create a standard squared exponential covariance function:
62 Create a squared exponential covariance function specifying the scale vector (amplitude is fixed to…
67 Create a squared exponential covariance function specifying the scale vector and the amplitude :
H A DCovarianceModelFactoryImplementation_doc.i.in2 "Estimation of the covariance model of a process.
6 …an interface class for all the classes that build covariance models. OpenTURNS provides two covari…
H A DKrigingResult_doc.i.in103 "Accessor to the covariance coefficients.
125 "Accessor to the covariance model.
130 The covariance model of the Gaussian process *W* with its optimized parameters.
188 "Compute the conditional covariance of the Gaussian process on a point (or several points).
198 The point :math:`\vect{x}` where the conditional covariance of the output has to be evaluated.
205 …The conditional covariance :math:`\Cov{\vect{Y}(\omega, \vect{x})\, | \, \cC}` at point :math:`\v…
206 Or the conditional covariance matrix at the sample :math:`(\vect{\xi}_1, \dots, \vect{\xi}_M)`:
223 "Compute the conditional covariance of the Gaussian process on a point (or several points).
234 …The point :math:`\vect{x}` where the conditional marginal covariance of the output has to be evalu…
241 …The conditional covariance :math:`\Cov{\vect{Y}(\omega, \vect{x})\, | \, \cC}` at point :math:`\v…
[all …]
/dports/math/R-cran-lava/lava/tests/testthat/
H A Dtest-constrain.R26 covariance(m,y1~y2) <- "C"
64 m <- covariance(lvm(),X1~X2)
93 covariance(l) <- bw.1 ~ bw.2
105 covariance(l) <- bw.1 ~ bw.2
107 covariance(l,~bw.1+bw.2) <- "s"
108 covariance(l,bw.1~bw.2) <- "r1"
110 covariance(l2,bw.1~bw.2) <- "r2"
/dports/math/octave-forge-stk/stk/inst/examples/01_kriging_basics/
H A Dstk_example_kb01.m9 % A Matern covariance function is used for the Gaussian Process (GP) prior.
10 % The parameters of this covariance function are assumed to be known (i.e.,
68 % We choose a Matern covariance with "fixed parameters" (in other words, the
69 % parameters of the covariance function are provided by the user rather than
74 % kriging) and a Matern covariance function. (Some default parameters are also
78 % NOTE: the suffix '_iso' indicates an ISOTROPIC covariance function, but the
81 % Parameters for the Matern covariance function
/dports/science/dakota/dakota-6.13.0-release-public.src-UI/packages/external/queso/test/test_gaussian_likelihoods/
H A Dtest_diagonalCovarianceChain.C46 const V & observations, const V & covariance) in Likelihood() argument
48 observations, covariance) in Likelihood()
158 this->covariance = new QUESO::GslVector(this->obsSpace->zeroVector()); in BayesianInverseProblem()
159 (*(this->covariance))[0] = 2.0; in BayesianInverseProblem()
160 (*(this->covariance))[1] = 8.0; in BayesianInverseProblem()
167 *(this->paramDomain), *(this->observations), *(this->covariance)); in BayesianInverseProblem()
217 QUESO::GslVector * covariance; variable
/dports/math/openturns/openturns-1.18/python/doc/pyplots/
H A DKarhunenLoeveValidation.py8 covariance = ot.AbsoluteExponential([1.0]) variable
9 algo = ot.KarhunenLoeveP1Algorithm(mesh, covariance, threshold)
11 process = ot.GaussianProcess(covariance, mesh)
/dports/math/cgal/CGAL-5.3/examples/Solver_interface/
H A Ddiagonalize_matrix.cpp14 Eigen_matrix covariance = {{ 0., 0., 0., 0., 0., 0. }}; in main() local
18 covariance[i] = rand(); in main()
23 if(!(Diagonalize_traits::diagonalize_selfadjoint_covariance_matrix(covariance, in main()
/dports/science/InsightToolkit/ITK-5.0.1/Modules/Core/ImageFunction/test/
H A DitkCovarianceImageFunctionTest.cxx79 FunctionType::OutputType covariance; in itkCovarianceImageFunctionTest() local
81 covariance = function->EvaluateAtIndex( index ); in itkCovarianceImageFunctionTest()
82 std::cout << "function->EvaluateAtIndex( index ): " << covariance << std::endl; in itkCovarianceImageFunctionTest()
110 if( ! itk::Math::FloatAlmostEqual( itk::Math::abs( covariance[ix][iy] ), in itkCovarianceImageFunctionTest()
/dports/math/stan/stan-2.28.2/src/stan/mcmc/
H A Dchains.hpp71 using boost::accumulators::tag::covariance; in covariance()
75 accumulator_set<double, stats<covariance<double, covariate1> > > acc; in covariance()
81 return boost::accumulators::covariance(acc) * M / (M - 1); in covariance()
90 using boost::accumulators::tag::covariance; in correlation()
104 double cov = boost::accumulators::covariance(acc_xy); in correlation()
450 return covariance(samples(chain, index1), samples(chain, index2)); in covariance()
453 double covariance(const int index1, const int index2) const { in covariance() function in stan::mcmc::chains
454 return covariance(samples(index1), samples(index2)); in covariance()
457 double covariance(const int chain, const std::string& name1, in covariance() function in stan::mcmc::chains
459 return covariance(chain, index(name1), index(name2)); in covariance()
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/dports/math/py-pystan/pystan-2.19.0.0/pystan/stan/src/stan/mcmc/
H A Dchains.hpp67 static double covariance(const Eigen::VectorXd& x, in covariance() function in stan::mcmc::chains
75 using boost::accumulators::tag::covariance; in covariance()
78 accumulator_set<double, stats<covariance<double, covariate1> > > acc; in covariance()
84 return boost::accumulators::covariance(acc) * M / (M-1); in covariance()
95 using boost::accumulators::tag::covariance; in correlation()
108 double cov = boost::accumulators::covariance(acc_xy); in correlation()
489 double covariance(const int index1, const int index2) const { in covariance() function in stan::mcmc::chains
490 return covariance(samples(index1), samples(index2)); in covariance()
493 double covariance(const int chain, const std::string& name1, in covariance() function in stan::mcmc::chains
495 return covariance(chain, index(name1), index(name2)); in covariance()
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/dports/biology/migrate/migrate-3.6.11/src/
H A Dcorrelation.c144 …->bayes->numparams,world->bayes->params, offset, locus,&world->bayes->histogram[locus].covariance); in covariance_bayes()
159 if(world->bayes->histogram[0].covariance==NULL) in covariance_summary()
161 if (target->covariance==NULL) in covariance_summary()
163 doublevec2d(&target->covariance,nn,nn); in covariance_summary()
169 cov = world->bayes->histogram[locus].covariance; in covariance_summary()
174 target->covariance[i][j] += cov[i][j]*invn; in covariance_summary()
/dports/math/py-pandas/pandas-1.2.5/doc/source/user_guide/
H A Dcomputation.rst34 .. _computation.covariance:
39 :meth:`Series.cov` can be used to compute covariance between series
51 .. _computation.covariance.caveats:
56 for the covariance matrix which is unbiased. However, for many applications
57 this estimate may not be acceptable because the estimated covariance matrix
60 and/or a non-invertible covariance matrix. See `Estimation of covariance
112 Please see the :ref:`caveats <computation.covariance.caveats>` associated
114 :ref:`covariance section <computation.covariance>`.
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/_smoothers/
H A D_classical.pyx77 # Factorize the predicted state covariance matrix
110 # Scaled smoothed estimator covariance matrix
194 # Smoothed state covariance
229 # Factorize the predicted state covariance matrix
262 # Scaled smoothed estimator covariance matrix
346 # Smoothed state covariance
381 # Factorize the predicted state covariance matrix
414 # Scaled smoothed estimator covariance matrix
498 # Smoothed state covariance
566 # Scaled smoothed estimator covariance matrix
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/dports/sysutils/xvidcap/xvidcap-1.1.7/ffmpeg/libavutil/
H A Dlls.c48 m->covariance[i][j] *= decay; in av_update_lls()
49 m->covariance[i][j] += var[i]*var[j]; in av_update_lls()
56 double (*factor)[MAX_VARS+1]= (void*)&m->covariance[1][0]; in av_solve_lls()
57 double (*covar )[MAX_VARS+1]= (void*)&m->covariance[1][1]; in av_solve_lls()
58 double *covar_y = m->covariance[0]; in av_solve_lls()
/dports/math/R-cran-car/car/man/
H A Dinfluence-mixed-models.Rd15 …link{dfbetas}}, \code{\link{cooks.distance}}, and influence on variance-covariance components based
40 …on the fixed effects; if \code{"var.cov"}, return influence on the variance-covariance components.}
45 \code{influence.lme} starts with the estimated variance-covariance components from \code{model} and…
50 influence on the variance-covariance components.
61 \item{\code{"var.cov.comps"}}{the estimated variance-covariance parameters for the model.}
62 …\item{\code{"var.cov.comps[-groups]"}}{a matrix with the estimated covariance parameters (in colum…
63 \item{\code{"vcov"}}{The estimated covariance matrix of the fixed-effects coefficients.}
64 …\item{\code{"vcov[-groups]"}}{a list each of whose elements is the estimated covariance matrix of …
H A Dhccm.Rd17 Calculates heteroscedasticity-corrected covariance matrices
20 covariance matrices.
37 produces an error; otherwise, the aliased coefficients are ignored in the coefficient covariance
43 …The original White-corrected coefficient covariance matrix (\code{"hc0"}) for an unweighted model …
47 corrected covariance matrix.
54 The heteroscedasticity-corrected covariance matrix for the model.
77 A heteroskedastic consistent covariance matrix estimator and a direct test of heteroskedasticity.
/dports/math/R-cran-fracdiff/fracdiff/tests/
H A Dex.Rout.save41 [5] "ar" "ma" "covariance.dpq" "fnormMin"
54 + c("h", "covariance.dpq", "stderror.dpq", "correlation.dpq", "hessian.dpq")
56 > dns <- dimnames(fd1.$covariance.dpq)
61 + covariance.dpq = matrix(c(0.0005966, -0.0008052, -0.0001897,
79 + covariance.dpq = matrix(c(0.0005966, -0.0008052, -0.0001897,
105 + covariance.dpq = matrix(c(0.0004182859, -0.0007078449, -6.753008e-05,
134 + covariance.dpq = matrix(c( 5.4726e-05,-9.261e-05, -8.8353e-06,
156 + covariance.dpq = matrix(c(-0.0003545, 6e-04, 5.724e-05,
/dports/math/cgal/CGAL-5.3/include/CGAL/
H A Dhierarchy_simplify_point_set.h244 std::array<FT, 6> covariance = {{ 0., 0., 0., 0., 0., 0. }}; in hierarchy_simplify_point_set() local
251 covariance[0] += d.x () * d.x (); in hierarchy_simplify_point_set()
252 covariance[1] += d.x () * d.y (); in hierarchy_simplify_point_set()
253 covariance[2] += d.x () * d.z (); in hierarchy_simplify_point_set()
254 covariance[3] += d.y () * d.y (); in hierarchy_simplify_point_set()
255 covariance[4] += d.y () * d.z (); in hierarchy_simplify_point_set()
256 covariance[5] += d.z () * d.z (); in hierarchy_simplify_point_set()
266 (covariance, eigenvalues, eigenvectors); in hierarchy_simplify_point_set()
/dports/benchmarks/phoronix-test-suite/phoronix-test-suite-10.6.1/ob-cache/test-profiles/pts/polybench-c-1.2.0/
H A Dinstall.sh16 cc $CFLAGS -I utilities -I datamining/covariance utilities/polybench.c datamining/covariance/covari…

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