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Worked out in terms of W, needs gradients implementing

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This commit is contained in:
Alan Saul 2013-03-19 18:21:57 +00:00
parent 46d59c94b2
commit a9d5555976
3 changed files with 57 additions and 40 deletions
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5
python/likelihoods/likelihood_function.py
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@ -19,6 +19,9 @@ class student_t(likelihood_function):
self.v = deg_free
self.sigma = sigma
#FIXME: This should be in the superclass
self.log_concave = False
def link_function(self, y, f):
"""link_function $\ln p(y|f)$
$$\ln p(y_{i}|f_{i}) = \ln \Gamma(\frac{v+1}{2}) - \ln \Gamma(\frac{v}{2})\sqrt{v \pi}\sigma - \frac{v+1}{2}\ln (1 + \frac{1}{v}\left(\frac{y_{i} - f_{i}}{\sigma}\right)^2$$
@ -70,7 +73,7 @@ class student_t(likelihood_function):
assert y.shape == f.shape
e = y - f
#hess = ((self.v + 1) * e) / ((((self.sigma**2) * self.v) + e**2)**2)
hess = ((self.v + 1) * (e**2 - self.v*(self.sigma**2))) / ((((self.sigma**2) * self.v) + e**2)**2)
hess = ((self.v + 1)*(e**2 - self.v*(self.sigma**2))) / ((((self.sigma**2)*self.v) + e**2)**2)
return hess
def predictive_values(self, mu, var):