Special Functions for Applied Scientists by A. M. Mathai, Hans J. Haubold (auth.)

By A. M. Mathai, Hans J. Haubold (auth.)

Special capabilities for utilized Scientists provides the necessary mathematical instruments for researchers energetic within the actual sciences. The ebook provides a whole swimsuit of undemanding capabilities for students on the PhD point and covers a wide-array of subject matters and starts off through introducing straightforward classical specified services. From there, differential equations and a few functions into statistical distribution idea are examined.

The fractional calculus bankruptcy covers fractional integrals and fractional derivatives in addition to their functions to reaction-diffusion difficulties in physics, input-output research, Mittag-Leffler stochastic strategies and similar subject matters. The authors then hide q-hypergeometric capabilities, Ramanujan's paintings and Lie teams.

The latter half this quantity provides functions into stochastic methods, random variables, Mittag-Leffler approaches, density estimation, order records, and difficulties in astrophysics.

Professor Dr. A.M. Mathai is Emeritus Professor of arithmetic and records, McGill college, Canada.

Professor Dr. Hans J. Haubold is an astrophysicist and leader scientist on the place of work of Outer area Affairs of the United Nations.

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By A. M. Mathai, Hans J. Haubold (auth.)

Special capabilities for utilized Scientists provides the necessary mathematical instruments for researchers energetic within the actual sciences. The ebook provides a whole swimsuit of undemanding capabilities for students on the PhD point and covers a wide-array of subject matters and starts off through introducing straightforward classical specified services. From there, differential equations and a few functions into statistical distribution idea are examined.

The fractional calculus bankruptcy covers fractional integrals and fractional derivatives in addition to their functions to reaction-diffusion difficulties in physics, input-output research, Mittag-Leffler stochastic strategies and similar subject matters. The authors then hide q-hypergeometric capabilities, Ramanujan's paintings and Lie teams.

The latter half this quantity provides functions into stochastic methods, random variables, Mittag-Leffler approaches, density estimation, order records, and difficulties in astrophysics.

Professor Dr. A.M. Mathai is Emeritus Professor of arithmetic and records, McGill college, Canada.

Professor Dr. Hans J. Haubold is an astrophysicist and leader scientist on the place of work of Outer area Affairs of the United Nations.

Show description

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16). 17) m m For m = 2 we obtain the duplication formula for gamma functions, namely, Γ(2z) = (2π ) 1−2 2 1 22z− 2 Γ(z)Γ z + 1 2 1 = π − 2 22z−1 Γ(z)Γ z + 1 . 17). For example, 1 = Γ(1) = Γ 2 1 2 = π − 2 21−1 Γ 1 = Γ(1) = Γ 3 1 3 = (2π ) 1 1−3 2 √ 1 1 1 1 1 1 + Γ = π− 2 Γ ⇒Γ = π. 2 2 2 2 2 1− 1 3 2 Γ 1 2 1 2 Γ Γ(1) ⇒ Γ Γ 3 3 3 3 2π =√ . 3 By using the product formulae for trigonometric functions we can establish the following results: Γ(z)Γ(1 − z) = π cosec π z π Γ(z)Γ(−z) = − cosec π z z 1 1 +z Γ − z = π sec π z.

Similarly if (x1 , · · · , xk ) have a type-2 Dirichlet density then any subset of them will have a type-2 Dirichlet density. 2. Evaluate the marginal densities from the following bivariate density: Γ(α1 + α2 + α3 ) α1 −1 α2 −1 x x2 (1 − x1 − x2 )α3 −1 , 0 ≤ x j ≤ 1, j = 1, 2, 3, Γ(α1 )Γ(α2 )Γ(α3 ) 1 0 ≤ x1 + x2 + x3 ≤ 1, ℜ(α j ) > 0, j = 1, 2, 3, and f (x1 , x2 ) = 0 elsewhere. 2: Let the marginal densities be denoted by f1 (x1 ) and f2 (x2 ) respectively. f1 (x1 ) = x2 × f (x1 , x2 )dx2 = 1−x1 x2 =0 Γ(α1 + α2 + α3 ) α1 −1 x Γ(α1 )Γ(α2 )Γ(α3 ) 1 x2α2 −1 (1 − x1 − x2 )α3 −1 dx2 Γ(α1 + α2 + α3 ) α1 −1 x = (1 − x1 )α3 −1 Γ(α1 )Γ(α2 )Γ(α3 ) 1 1−x1 x2 =0 x2α2 −1 1 − Put, for fixed x1 , y2 = x2 ⇒ dx2 = (1 − x1 )dy2 .

X=1 The expected value of the Poisson random variable is the parameter itself. 2. µr = E(xr ) is called the rth moment of x and E(x − E(x))r = µr is called the rth central moment of x. µ1 = E(x) is also called the mean value of x or the centre of gravity in x. When x is discrete, taking values x1 , · · · , xk with the corresponding probabilities p1 , · · · , pk , pi ≥ 0, i = 1, · · · , k, p1 + · · · + pk = 1 then k E(x) = ∑ pi xi = i=1 This can be considered to be a physical system with weights p1 , · · · , pk at x1 , · · · , xk then E(x) is the center of gravity of the system.

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