def prob(self, gauss):
"""Returns the probability of drawing the provided Gaussian from this prior."""
d = self.mu.shape[0]
wishart = Wishart(d)
gaussian = Gaussian(d)
wishart.setDof(self.n)
wishart.setScale(self.getLambda())
gaussian.setMean(self.mu)
gaussian.setPrecision(self.k*gauss.getPrecision())
return wishart.prob(gauss.getPrecision()) * gaussian.prob(gauss.getMean())
def prob(self, gauss):
"""Returns the probability of drawing the provided Gaussian from this prior."""
d = self.mu.shape[0]
wishart = Wishart(d)
gaussian = Gaussian(d)
wishart.setDof(self.n)
wishart.setScale(self.getLambda())
gaussian.setMean(self.mu)
gaussian.setPrecision(self.k * gauss.getPrecision())
return wishart.prob(gauss.getPrecision()) * gaussian.prob(gauss.getMean())
def sample(self):
"""Returns a Gaussian, drawn from this prior."""
d = self.mu.shape[0]
wishart = Wishart(d)
gaussian = Gaussian(d)
ret = Gaussian(d)
wishart.setDof(self.n)
wishart.setScale(self.getLambda())
ret.setPrecision(wishart.sample())
gaussian.setPrecision(self.k * ret.getPrecision())
gaussian.setMean(self.mu)
ret.setMean(gaussian.sample())
return ret
def sample(self):
"""Returns a Gaussian, drawn from this prior."""
d = self.mu.shape[0]
wishart = Wishart(d)
gaussian = Gaussian(d)
ret = Gaussian(d)
wishart.setDof(self.n)
wishart.setScale(self.getLambda())
ret.setPrecision(wishart.sample())
gaussian.setPrecision(self.k * ret.getPrecision())
gaussian.setMean(self.mu)
ret.setMean(gaussian.sample())
return ret