- #1
unchained1978
- 93
- 0
I'm dealing with multivariate normal distributions, and I've run up against an integral I really don't know how to do.
Given a random vector [itex]\vec x[/itex], and a covariance matrix [itex]\Sigma[/itex], how would you go about evaluating an expectation value of the form
[itex]G=\int d^{n} x \left(\prod_{i=1}^{n} f_{i}(x_{i})\right) e^{-\frac{1}{2} \vec x \cdot \Sigma^{-1}\cdot \vec x}[/itex]
I've tried expanding the [itex]f_{i}'s[/itex] into a power series, and then using the moment generating function to obtain the powers of [itex]x_{i}[/itex], but this really doesn't simplify the problem much. The function I'm currently considering is [itex]f_{i}(x_{i})=\cosh(x_{i})[/itex].
I would extremely appreciate anyone's input on this problem.
Given a random vector [itex]\vec x[/itex], and a covariance matrix [itex]\Sigma[/itex], how would you go about evaluating an expectation value of the form
[itex]G=\int d^{n} x \left(\prod_{i=1}^{n} f_{i}(x_{i})\right) e^{-\frac{1}{2} \vec x \cdot \Sigma^{-1}\cdot \vec x}[/itex]
I've tried expanding the [itex]f_{i}'s[/itex] into a power series, and then using the moment generating function to obtain the powers of [itex]x_{i}[/itex], but this really doesn't simplify the problem much. The function I'm currently considering is [itex]f_{i}(x_{i})=\cosh(x_{i})[/itex].
I would extremely appreciate anyone's input on this problem.