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gptkb:gamma_distribution
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f(x;k,θ) = x^{k-1} e^{-x/θ} / (θ^k Γ(k))
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gptkb:Generative_Pre-trained_Transformer_1
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https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf
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gptkb:Pearson_Type_VII
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f(x) = C [1 + ((x-μ)^2)/(m a^2)]^{-m}
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gptkb:chi_distribution
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(1/2^{k/2-1} Gamma(k/2)) x^{k-1} e^{-x^2/2}
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gptkb:Pearson_Type_VI
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f(x) = (a^m * x^{m-1}) / (B(m, n) * (a + x)^{m+n}) for x > 0
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gptkb:Noncentral_chi-squared_distribution
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Involves modified Bessel function
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gptkb:standard_normal_distribution
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(1/sqrt(2π)) * exp(-x^2/2)
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gptkb:Chi-squared_distribution
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f(x; k) = (1/(2^{k/2} Γ(k/2))) x^{k/2-1} e^{-x/2}
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gptkb:DeBERTa-Large
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https://arxiv.org/abs/2006.03654
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gptkb:Nakagami_distribution
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(2m^m)/(Γ(m)Ω^m) x^{2m-1} exp(-m x^2/Ω), x ≥ 0
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gptkb:Pearson_Type_VI_distribution
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f(x; α1, α2, β) = (x/β)^{α1-1} / [β B(α1, α2) (1 + x/β)^{α1+α2}] for x > 0
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gptkb:Landau_distribution
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no closed-form expression
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gptkb:Onyx_Boox_Max_2
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yes
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gptkb:arXiv:1411.4028
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https://arxiv.org/pdf/1411.4028.pdf
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gptkb:YOLOX
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https://arxiv.org/abs/2107.08430
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gptkb:Lévy_distribution
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f(x; μ, c) = sqrt(c / (2π)) * exp(-c / (2(x-μ))) / (x-μ)^{3/2}, x > μ
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gptkb:Variance_Gamma_distribution
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complex formula involving modified Bessel function
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gptkb:Chi-squared_distribution_(λ=0)
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(1/(2^{k/2}Γ(k/2))) x^{k/2-1} e^{-x/2}
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gptkb:Snedecor's_F-distribution
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f(x; d1, d2) = [d1^{d1/2} d2^{d2/2} x^{(d1/2)-1}] / [B(d1/2, d2/2) (d1 x + d2)^{(d1+d2)/2}]
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gptkb:Onyx_Boox_Max_Lumi
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yes
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