statsmodels.tools.numdiff.approx_hess2(x, f, epsilon=None, args=(), kwargs={}, return_grad=False)
[source]
Calculate Hessian with finite difference derivative approximation
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Equation (8) in Ridout. Computes the Hessian as:
1/(2*d_j*d_k) * ((f(x + d[j]*e[j] + d[k]*e[k]) - f(x + d[j]*e[j])) - (f(x + d[k]*e[k]) - f(x)) + (f(x - d[j]*e[j] - d[k]*e[k]) - f(x + d[j]*e[j])) - (f(x - d[k]*e[k]) - f(x)))
where e[j] is a vector with element j == 1 and the rest are zero and d[i] is epsilon[i].
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tools.numdiff.approx_hess2.html