def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     k = dist.k
     tau_e = dist.tau_e
     return r'${} \sim \text{{AR1}}(\mathit{{k}}={}, \mathit{{tau_e}}={})$'.format(name,
              get_variable_name(k), get_variable_name(tau_e))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     nu = dist.nu
     V = dist.V
     return r'${} \sim \text{{Wishart}}(\mathit{{nu}}={}, \mathit{{V}}={})$'.format(name,
                                             get_variable_name(nu),
                                             get_variable_name(V))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     q = dist.q
     beta = dist.beta
     return r'${} \sim \text{{DiscreteWeibull}}(\mathit{{q}}={}, \mathit{{beta}}={})$'.format(name,
                                             get_variable_name(q),
                                             get_variable_name(beta))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     mu = dist.mu
     sd = dist.sd
     return r'${} \sim \text{{GaussianRandomWalk}}(\mathit{{mu}}={}, \mathit{{sd}}={})$'.format(name,
                                             get_variable_name(mu),
                                             get_variable_name(sd))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     name_eta = get_variable_name(dist.eta)
     name_cutpoints = get_variable_name(dist.cutpoints)
     return (r'${} \sim \text{{OrderedLogistic}}'
             r'(\mathit{{eta}}={}, \mathit{{cutpoints}}={}$'
             .format(name, name_eta, name_cutpoints))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     alpha = dist.alpha
     beta = dist.beta
     name = r'\text{%s}' % name
     return r'${} \sim \text{{NegativeBinomial}}(\mathit{{alpha}}={},~\mathit{{beta}}={})$'.format(name,
                                             get_variable_name(alpha),
                                             get_variable_name(beta))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     mu = dist.mu
     sigma = dist.sigma
     name = r'\text{%s}' % name
     return r'${} \sim \text{{GaussianRandomWalk}}(\mathit{{mu}}={},~\mathit{{sigma}}={})$'.format(name,
                                             get_variable_name(mu),
                                             get_variable_name(sigma))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     mu = dist.mu
     w = dist.w
     sd = dist.sd
     return r'${} \sim \text{{NormalMixture}}(\mathit{{w}}={}, \mathit{{mu}}={}, \mathit{{sigma}}={})$'.format(name,
                                             get_variable_name(w),
                                             get_variable_name(mu),
                                             get_variable_name(sd))

 def _repr_latex_(self, name=None, dist=None):
     if dist is None:
         dist = self
     n = dist.n
     p = dist.p
     psi = dist.psi
     return r'${} \sim \text{{ZeroInflatedBinomial}}(\mathit{{n}}={}, \mathit{{p}}={}, \mathit{{psi}}={})$'.format(name,
                                             get_variable_name(n),
                                             get_variable_name(p),
                                             get_variable_name(psi))

 def _repr_cov_params(self, dist=None):
     if dist is None:
         dist = self
     if self._cov_type == 'chol':
         chol = get_variable_name(self.chol)
         return r'\mathit{{chol}}={}'.format(chol)
     elif self._cov_type == 'cov':
         cov = get_variable_name(self.cov)
         return r'\mathit{{cov}}={}'.format(cov)
     elif self._cov_type == 'tau':
         tau = get_variable_name(self.tau)
         return r'\mathit{{tau}}={}'.format(tau)