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21: 6 Exponential, Logarithmic, Sine, and
Cosine Integrals
22: 35.9 Applications
In multivariate statistical analysis based on the multivariate normal distribution, the probability density functions of many random matrices are expressible in terms of generalized hypergeometric functions of matrix argument F q p , with p 2 and q 1 . …
23: 18.17 Integrals
18.17.15 e x L n ( α ) ( x ) = x e y L n ( α + μ ) ( y ) ( y x ) μ 1 Γ ( μ ) d y , μ > 0 .
18.17.28_5 0 e x x α L n ( α ) ( x ) e i x y d x = Γ ( α + n + 1 ) ( i y ) n n ! ( 1 i y ) α + n + 1 ,
18.17.34 0 e x z L n ( α ) ( x ) e x x α d x = Γ ( α + n + 1 ) z n n ! ( z + 1 ) α + n + 1 , z > 1 .
18.17.34_5 0 e x z L m ( α ) ( x ) L n ( α ) ( x ) e x x α d x = Γ ( α + m + 1 ) Γ ( α + n + 1 ) Γ ( α + 1 ) m ! n ! z m + n ( z + 1 ) α + m + n + 1 F 1 2 ( m , n α + 1 ; z 2 ) , z > 1 .
18.17.40 0 e a x L n ( α ) ( b x ) x z 1 d x = Γ ( z + n ) n ! ( a b ) n a n z F 1 2 ( n , 1 + α z 1 n z ; a a b ) , a > 0 , z > 0 .
24: 16.5 Integral Representations and Integrals
25: 18.10 Integral Representations
18.10.6 L n ( α ) ( x 2 ) = 2 ( 1 ) n π 1 2 Γ ( α + 1 2 ) n ! 0 0 π ( x 2 r 2 + 2 i x r cos ϕ ) n e r 2 r 2 α + 1 ( sin ϕ ) 2 α d ϕ d r , α > 1 2 .
18.10.9 L n ( α ) ( x ) = e x x 1 2 α n ! 0 e t t n + 1 2 α J α ( 2 x t ) d t , α > 1 .
26: 13.6 Relations to Other Functions
13.6.6 U ( a , a , z ) = z 1 a U ( 1 , 2 a , z ) = z 1 a e z E a ( z ) = e z Γ ( 1 a , z ) .
27: 10.74 Methods of Computation
For evaluation of the Hankel functions H ν ( 1 ) ( z ) and H ν ( 2 ) ( z ) for complex values of ν and z based on the integral representations (10.9.18) see Remenets (1973). For applications of generalized Gauss–Laguerre quadrature (§3.5(v)) to the evaluation of the modified Bessel functions K ν ( z ) for 0 < ν < 1 and 0 < x < see Gautschi (2002a). The integral representation used is based on (10.32.8). … Then J n ( x ) and Y n ( x ) can be generated by either forward or backward recurrence on n when n < x , but if n > x then to maintain stability J n ( x ) has to be generated by backward recurrence on n , and Y n ( x ) has to be generated by forward recurrence on n . …
28: 14.2 Differential Equations
14.2.11 P ν + 1 μ ( x ) Q ν μ ( x ) P ν μ ( x ) Q ν + 1 μ ( x ) = e μ π i Γ ( ν + μ + 1 ) Γ ( ν μ + 2 ) .
29: 14.3 Definitions and Hypergeometric Representations
14.3.7 Q ν μ ( x ) = e μ π i π 1 / 2 Γ ( ν + μ + 1 ) ( x 2 1 ) μ / 2 2 ν + 1 x ν + μ + 1 𝐅 ( 1 2 ν + 1 2 μ + 1 , 1 2 ν + 1 2 μ + 1 2 ; ν + 3 2 ; 1 x 2 ) , μ + ν 1 , 2 , 3 , .
30: 9.13 Generalized Airy Functions
9.13.9 A n ( z ) = p / π sin ( p π ) z n / 4 e ζ ( 1 + O ( ζ 1 ) ) , | ph z | 3 p π δ ,
9.13.10 A n ( z ) = { 2 p / π cos ( 1 2 p π ) z n / 4 ( cos ( ζ 1 4 π ) + e | ζ | O ( ζ 1 ) ) , | ph z | 2 p π δ n  odd , p / π z n / 4 e ζ ( 1 + O ( ζ 1 ) ) , | ph z | p π δ n  even ,
9.13.11 B n ( z ) = π 1 / 2 z n / 4 e ζ ( 1 + O ( ζ 1 ) ) , | ph z | p π δ ,
9.13.12 B n ( z ) = { ( 2 / π ) sin ( 1 2 p π ) z n / 4 ( sin ( ζ 1 4 π ) + e | ζ | O ( ζ 1 ) ) , | ph z | 2 p π δ , n  odd , ( 1 / π ) sin ( p π ) z n / 4 e ζ ( 1 + O ( ζ 1 ) ) , | ph z | 3 p π δ , n  even .
9.13.18 w = U m ( t e 2 j π i / m ) , j = 0 , ± 1 , ± 2 , .