def PX(self, t, xp):
return dists.IndepProd(dists.Normal(loc=xp[:, 0], scale=self.sigmaX),
dists.Normal(loc=xp[:, 1], scale=self.sigmaX),
dists.Dirac(loc=xp[:, 0] + xp[:, 2]),
dists.Dirac(loc=xp[:, 1] + xp[:, 3])
)
def PX0(self):
return dists.IndepProd(dists.Normal(loc=self.x0[0], scale=self.sigmaX),
dists.Normal(loc=self.x0[1], scale=self.sigmaX),
dists.Dirac(loc=self.x0[2]),
dists.Dirac(loc=self.x0[3])
)
def PY(self, t, xp, x):
return dists.IndepProd(*[dists.Poisson(np.exp(self.a[k] +
np.sum(x*self.b[k], axis=1)))
for k in range(self.dy)])
def PX0(self):
return dists.IndepProd(*[dists.Dirac(loc=self.x0[i])
for i in range(6)])
def PY(self, t, xp, x):
return dists.IndepProd(
NegativeBinomial(n=1 / self.tau, p=1 / (1 + self.tau * np.exp(x))),
NegativeBinomial(n=1 / self.tau, p=1 / (1 + self.tau * np.exp(x))))
def PY(self, t, xp, x):
return dists.IndepProd(dists.Poisson(rate=np.exp(x)),
dists.Poisson(rate=np.exp(x)))
