Python uniform Examples

Example #1

def linear_model_logp(b, sig):
    if smp.outofbounds(sig > 0):
        return -np.inf
    mu = np.dot(b, x)
    n = len(data)
    likelihood = -n*0.5*np.log(2*np.pi) - \
                  n*0.5*np.log(sig**2) - \
                  np.sum((data - mu)**2)/(2*sig**2)
    prior_sig = -np.log(np.abs(sig))
    prior_b = smp.uniform(b, lower=-5, upper=10)
    return likelihood + prior_sig + prior_b

Example #2

File: logps.py Project: CamZHU/sampyl

def linear_model_logp(b, sig):
    if smp.outofbounds(sig > 0):
        return -np.inf
    mu = np.dot(b, x)
    n = len(data)
    likelihood = -n*0.5*np.log(2*np.pi) - \
                  n*0.5*np.log(sig**2) - \
                  np.sum((data - mu)**2)/(2*sig**2)
    prior_sig = -np.log(np.abs(sig))
    prior_b = smp.uniform(b, lower=-5, upper=10)
    return likelihood + prior_sig + prior_b

Example #3

def normal_logp(mu, sig):
    likelihood = -n*0.5*np.log(sig**2) - \
                  np.sum((data - mu)**2)/(2*sig**2)
    mu_prior = smp.uniform(mu, 5, 15)
    sig_prior = -np.log(np.abs(sig))
    return likelihood + mu_prior + sig_prior

Example #4

File: stationary.py Project: thongnnguyen/HyperSphere

	def prior(self, vec):
		n_param_super = super(Stationary, self).n_params()
		return super(Stationary, self).prior(vec[:n_param_super]) + smp.uniform(np.exp(vec[n_param_super:]), lower=np.exp(log_lower_bnd), upper=np.exp(self.max_log_ls))

Example #5

File: logps.py Project: CamZHU/sampyl

def normal_logp(mu, sig):
    likelihood = -n*0.5*np.log(sig**2) - \
                  np.sum((data - mu)**2)/(2*sig**2)
    mu_prior = smp.uniform(mu, 5, 15)
    sig_prior = -np.log(np.abs(sig))
    return likelihood + mu_prior + sig_prior