| /third_party/boost/libs/math/doc/distributions/ |
| D | inverse_gaussian.qbk | 1 [section:inverse_gaussian_dist Inverse Gaussian (or Inverse Normal) Distribution]
25 The Inverse Gaussian distribution distribution is a continuous probability distribution.
27 The distribution is also called 'normal-inverse Gaussian distribution',
32 The Inverse Gaussian distribution was first studied in relation to Brownian motion.
33 In 1956 M.C.K. Tweedie used the name Inverse Gaussian because there is an inverse relationship
35 The inverse Gaussian is one of family of distributions that have been called the
49 The normal-inverse Gaussian distributions form
55 Weisstein, Eric W. "Inverse Gaussian Distribution." From MathWorld--A Wolfram Web Resource.]
153 #The Inverse Gaussian distribution : theory, methodology, and applications, Raj S. Chhikara, J. Ler…
154 #The Inverse Gaussian distribution : statistical theory and applications, Seshadri, V , ISBN - 0387…
[all …]
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| D | normal.qbk | 1 [section:normal_dist Normal (Gaussian) Distribution]
32 distribution: it is also known as the Gaussian Distribution.
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| D | normal_example.qbk | 5 [section:normal_misc Some Miscellaneous Examples of the Normal (Gaussian) Distribution]
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| /third_party/boost/libs/math/doc/quadrature/ |
| D | gauss_kronrod.qbk | 12 Gauss-Kronrod quadrature is an extension of Gaussian quadrature which provides an a posteriori erro…
14 The idea behind Gaussian quadrature is to choose /n/ nodes and weights in such a way that polynomia…
16 Instead, transcendental and numerically defined functions are integrated via Gaussian quadrature, a…
17 Gaussian quadrature alone (without some form of interval splitting) cannot answer this question.
19 It is possible to compute a Gaussian quadrature of order /n/ and another of order (say) /2n+1/, and…
20 However, this is not optimal, as the zeros of the Legendre polynomials (nodes of the Gaussian quadr…
21 Kronrod considered the problem of how to interleave nodes into a Gaussian quadrature in such a way …
23 …es (computed from the zeros of the Legendre-Stieltjes polynomials) to a Gaussian quadrature of ord…
107 In fact, in this case the error is very nearly equal to the error of the Gaussian quadrature formul…
108 whereas the Kronrod extension converges exponentially beyond the Gaussian estimate.
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| D | gauss.qbk | 59 The Gaussian quadrature routine support both real and complex-valued quadrature.
64 so it can be effectively computed via Gaussian quadrature using the following code:
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| /third_party/skia/third_party/externals/abseil-cpp/absl/random/ |
| D | mock_distributions_test.cc | 61 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
64 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
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| D | generators_test.cc | 133 absl::Gaussian<float>(*gen, 1.0, 1.0); in TestGaussian()
134 absl::Gaussian<double>(*gen, 1.0, 1.0); in TestGaussian()
135 absl::Gaussian<double>(URBG(), 1.0, 1.0); in TestGaussian()
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| D | mocking_bit_gen_test.cc | 67 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
70 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
110 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
113 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
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| D | distributions_test.cc | 429 TEST_F(RandomDistributionsTest, Gaussian) { in TEST_F() argument
434 values[i] = absl::Gaussian<double>(gen); in TEST_F()
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| /third_party/abseil-cpp/absl/random/ |
| D | mock_distributions_test.cc | 61 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
64 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
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| D | generators_test.cc | 133 absl::Gaussian<float>(*gen, 1.0, 1.0); in TestGaussian()
134 absl::Gaussian<double>(*gen, 1.0, 1.0); in TestGaussian()
135 absl::Gaussian<double>(URBG(), 1.0, 1.0); in TestGaussian()
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| D | mocking_bit_gen_test.cc | 64 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
67 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
107 EXPECT_NE(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
110 EXPECT_EQ(absl::Gaussian<double>(gen, 0.0, 1.0), 0.001); in TEST()
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| D | distributions_test.cc | 419 TEST_F(RandomDistributionsTest, Gaussian) { in TEST_F() argument
424 values[i] = absl::Gaussian<double>(gen); in TEST_F()
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| /third_party/flutter/skia/src/gpu/effects/ |
| D | GrCircleBlurFragmentProcessor.fp | 53 // Create a Gaussian half-kernel (right side) and a summed area table given a sigma and number
95 // Apply a Gaussian at point (evalX, 0) to a circle centered at the origin with radius circleR.
96 // This relies on having a half kernel computed for the Gaussian and a table of applications of
124 // half the Gaussian and a matching summed area table. The summed area table is used to compute
155 // Ensure the tail of the Gaussian goes to zero.
198 // When sigma is really small this becomes a equivalent to convolving a Gaussian with a
200 // Guassian and the profile texture is a just a Gaussian evaluation. However, we haven't yet
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| D | GrRectBlurEffect.fp | 31 // We promote the math that gets us into the Gaussian space to full float when the rect
71 // a Gaussian as three box filters, and then computes the integral of this approximation from
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| /third_party/skia/third_party/externals/abseil-cpp/absl/container/internal/ |
| D | raw_hash_set_probe_benchmark.cc | 341 struct Gaussian { struct
346 d = absl::Gaussian<double>(GlobalBitGen(), 1e6, 1e4); in operator ()()
445 std::string Name(Random<T, Gaussian>*) { in Name() argument
498 RunForTypeAndDistribution<T, Random<T, Gaussian>>(results); in RunForType()
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| /third_party/boost/libs/gil/example/ |
| D | README.md | 26 Convolves the image with a Gaussian kernel.
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| /third_party/boost/libs/math/doc/statistics/ |
| D | signal_statistics.qbk | 152 // fill noisy_signal with data contaminated by Gaussian white noise:
159 … of the signal is 1 and the kurtosis of the noise is 3, the latter corresponding to Gaussian noise.
182 …ally not what you intend; for example a signal consisting of zeros plus Gaussian noise has an SNR …
183 whereas a signal with a constant DC bias and random Gaussian noise might have a very large SNR.
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| /third_party/boost/libs/gil/doc/image_processing/ |
| D | basics.rst | 53 would draw a flat plane, whereas Gaussian filter would draw a hill that
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| D | affine-region-detectors.rst | 76 **Weighted sum** is the same for both. Usually Gaussian blur
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| /third_party/boost/libs/gil/doc/html/_sources/image_processing/ |
| D | basics.rst.txt | 53 would draw a flat plane, whereas Gaussian filter would draw a hill that
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| /third_party/flutter/engine/flutter/lib/web_ui/lib/src/engine/surface/ |
| D | backdrop_filter.dart | 87 // Gaussian blur with standard deviation (normal distribution),
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| /third_party/boost/libs/math/doc/sf/ |
| D | legendre_stieltjes.qbk | 31 Gauss-Konrod quadratures are algorithms which extend a Gaussian quadrature in such a way that all a…
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| /third_party/ffmpeg/doc/ |
| D | scaler.texi | 45 Select Gaussian rescaling algorithm.
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| /third_party/boost/libs/math/dot_net_example/distribution_explorer/ |
| D | readme.txt | 36 * normal (Gaussian)
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