def transition_prior(rank, val, vec, F, hyperparams):
"""
Prior density for transition model parameters
"""
Psi0 = rank * hyperparams['rPsi0']
variancePrior = smp.singular_inverse_wishart_density(val, vec, Psi0)
orthVec = complete_basis(vec)
# relaxEval = self.hyperparams['alpha']
# relaxEval = np.min(model.parameters['val'])
relaxEval = np.max(val)
rowVariance = np.dot(vec, np.dot(np.diag(val), vec.T)) \
+ relaxEval*np.dot(orthVec,orthVec.T)
matrixPrior = smp.matrix_normal_density(F, hyperparams['M0'], rowVariance,
hyperparams['V0'])
return variancePrior + matrixPrior
def transition_prior(rank, val, vec, F, hyperparams):
"""
Prior density for transition model parameters
"""
Psi0 = rank*hyperparams['rPsi0']
variancePrior = smp.singular_inverse_wishart_density(val, vec, Psi0)
orthVec = complete_basis(vec)
# relaxEval = self.hyperparams['alpha']
# relaxEval = np.min(model.parameters['val'])
relaxEval = np.max(val)
rowVariance = np.dot(vec, np.dot(np.diag(val), vec.T)) \
+ relaxEval*np.dot(orthVec,orthVec.T)
matrixPrior = smp.matrix_normal_density(F,
hyperparams['M0'],
rowVariance,
hyperparams['V0'])
return variancePrior + matrixPrior
def transition_prior(self, model):
"""
Prior density for transition model parameters
"""
Psi0 = model.parameters['rank'][0] * self.hyperparams['rPsi0']
variancePrior = smp.singular_inverse_wishart_density(
model.parameters['val'], model.parameters['vec'], Psi0)
orthVec = model.complete_basis()
relaxEval = self.hyperparams['alpha']
# relaxEval = np.min(model.parameters['val'])
# relaxEval = np.max(model.parameters['val'])
rowVariance = model.transition_covariance() \
+ relaxEval*np.dot(orthVec,orthVec.T)
matrixPrior = smp.matrix_normal_density(model.parameters['F'],
self.hyperparams['M0'],
rowVariance,
self.hyperparams['V0'])
return variancePrior + matrixPrior
def transition_prior(self, model):
"""
Prior density for transition model parameters
"""
Psi0 = model.parameters['rank'][0]*self.hyperparams['rPsi0']
variancePrior = smp.singular_inverse_wishart_density(
model.parameters['val'],
model.parameters['vec'],
Psi0)
orthVec = model.complete_basis()
relaxEval = self.hyperparams['alpha']
# relaxEval = np.min(model.parameters['val'])
# relaxEval = np.max(model.parameters['val'])
rowVariance = model.transition_covariance() \
+ relaxEval*np.dot(orthVec,orthVec.T)
matrixPrior = smp.matrix_normal_density(model.parameters['F'],
self.hyperparams['M0'],
rowVariance,
self.hyperparams['V0'])
return variancePrior + matrixPrior
