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Normal Difference Distribution -- from Wolfram MathWorld

Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...



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Normal Difference Distribution -- from Wolfram MathWorld

https://mathworld.wolfram.com/NormalDifferenceDistribution.html

Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...



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https://mathworld.wolfram.com/NormalDifferenceDistribution.html

Normal Difference Distribution -- from Wolfram MathWorld

Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...

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      Normal Difference Distribution -- from Wolfram MathWorld
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      Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...
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      Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...

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      Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...

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      Amazingly, the distribution of a difference of two normally distributed variates X and Y with means and variances (mu_x,sigma_x^2) and (mu_y,sigma_y^2), respectively, is given by P_(X-Y)(u) = int_(-infty)^inftyint_(-infty)^infty(e^(-x^2/(2sigma_x^2)))/(sigma_xsqrt(2pi))(e^(-y^2/(2sigma_y^2)))/(sigma_ysqrt(2pi))delta((x-y)-u)dxdy (1) = (e^(-[u-(mu_x-mu_y)]^2/[2(sigma_x^2+sigma_y^2)]))/(sqrt(2pi(sigma_x^2+sigma_y^2))), (2) where delta(x) is a delta function, which is another normal...
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