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gptkb:chi-squared_distribution
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(1/(2^{k/2}Γ(k/2))) x^{(k/2)-1} e^{-x/2}
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gptkb:Standard_normal_distribution
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(1/sqrt(2π)) * exp(-x^2/2)
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gptkb:standard_multivariate_normal_distribution
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(2π)^(-n/2) exp(-1/2 x^T x)
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gptkb:ViT-H
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gptkb:An_Image_is_Worth_16x16_Words:_Transformers_for_Image_Recognition_at_Scale
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gptkb:Landau_distribution
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no closed-form expression
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gptkb:Log-gamma_distribution
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f(x; μ, θ, k) = (1/Γ(k)θ^k) exp(k(x-μ)/θ - exp((x-μ)/θ))
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gptkb:Onyx_Boox_Max_3
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yes
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gptkb:Inception_v1
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https://arxiv.org/abs/1409.4842
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gptkb:Onyx_Boox_Note
|
yes
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gptkb:Student's_t-distribution
|
f(x) = Γ((ν+1)/2) / (√(νπ) Γ(ν/2)) (1 + x²/ν)^(-(ν+1)/2)
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gptkb:generalized_inverse_Gaussian_distribution
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f(x) = (psi/chi)^(lambda/2) / (2 K_lambda(sqrt(chi psi))) x^(lambda-1) exp(-(chi/x + psi x)/2)
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gptkb:Standard_Normal_Distribution
|
(1/√(2π)) * exp(-x²/2)
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gptkb:Inverse_chi-squared_distribution
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f(x; ν) = (2^{−ν/2}/Γ(ν/2)) x^{−(ν/2)−1} exp(−1/(2x))
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gptkb:arXiv:1412.6980
|
https://arxiv.org/pdf/1412.6980.pdf
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gptkb:Pearson_Type_V_distribution
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f(x) = (b^a / Γ(a)) x^{-(a+1)} e^{-b/x}, x > 0
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gptkb:Pearson_Type_X_distribution
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f(x; α, β) = (x^{α-1} (1+x)^{-α-β}) / B(α, β)
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gptkb:Weibull_distribution
|
f(x; k, λ) = (k/λ) (x/λ)^{k-1} e^{-(x/λ)^k} for x ≥ 0
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gptkb:Variance_Gamma_distribution
|
complex formula involving modified Bessel function
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gptkb:univariate_t-distribution
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f(x) = Γ((ν+1)/2) / (√(νπ) Γ(ν/2)) (1 + x²/ν)^(-(ν+1)/2)
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gptkb:standard_normal_distribution
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(1/sqrt(2π)) * exp(-x^2/2)
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