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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:Multinomial_Naive_Bayes
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gptkb:A_Comparison_of_Event_Models_for_Naive_Bayes_Text_Classification
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gptkb:Pearson_Type_VII
|
f(x) = C [1 + ((x-μ)^2)/(m a^2)]^{-m}
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gptkb:Pearson_Type_V
|
f(x; a, b) = (b^a / Γ(a)) x^(-a-1) exp(-b/x)
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|
gptkb:2004.05150
|
https://arxiv.org/pdf/1706.03762.pdf
|
|
gptkb:Onyx_Boox_Max_2
|
yes
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gptkb:Lévy_distribution
|
f(x; μ, c) = sqrt(c / (2π)) * exp(-c / (2(x-μ))) / (x-μ)^{3/2}, x > μ
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gptkb:exponential_distribution
|
lambda * exp(-lambda * x)
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gptkb:Onyx_Boox_Tab_X
|
yes
|
|
gptkb:generalized_inverse_Gaussian_distribution
|
f(x) = (psi/chi)^(lambda/2) / (2 K_lambda(sqrt(chi psi))) x^(lambda-1) exp(-(chi/x + psi x)/2)
|
|
gptkb:normal_distribution
|
(1/(σ√(2π))) * exp(-0.5*((x-μ)/σ)^2)
|
|
gptkb:Fréchet_distribution
|
f(x; α, s, m) = (α/s) ((x-m)/s)^(-1-α) exp(-((x-m)/s)^(-α)) for x > m
|
|
gptkb:Inverse_chi-squared_distribution
|
f(x; ν) = (2^{−ν/2}/Γ(ν/2)) x^{−(ν/2)−1} exp(−1/(2x))
|
|
gptkb:DeepSeek-MoE
|
https://arxiv.org/abs/2405.13237
|
|
gptkb:arXiv:1411.4028
|
https://arxiv.org/pdf/1411.4028.pdf
|
|
gptkb:arXiv:1409.1556
|
https://arxiv.org/pdf/1409.1556.pdf
|
|
gptkb:Natural_Questions:_A_Benchmark_for_Question_Answering_Research
|
https://arxiv.org/abs/1906.00300
|
|
gptkb:triangular_distribution
|
piecewise linear
|
|
gptkb:Generative_Pre-trained_Transformer_3
|
gptkb:Language_Models_are_Few-Shot_Learners
|
|
gptkb:Johnson_SB_distribution
|
f(x) = (delta / (lambda * sqrt(2*pi))) * [1 / (y*(1-y))] * exp(-0.5 * [gamma + delta * ln(y/(1-y))]^2), where y = (x - xi)/lambda
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