class_docstr: TSM PARENT CLASS
  
  Contains general time series methods to be inherited by models.
  
  Parameters
  ----------
  model_type : str
      The type of model (e.g. 'ARIMA', 'GARCH')
class_docstr_tok: ["TSM","PARENT","CLASS","Contains","general","time","series","methods","to","be","inherited","by","models",".","Parameters","--","--","--","--","--","model_type",":","str","The","type","of","model","(","e.g",".","'ARIMA","'",",","'GARCH","'",")"]
docstr: Returns negative log posterior, for a model with a covariance matrix 
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent_variables
  
  Returns
  ----------
  Negative log posterior
docstr_tok: ["Returns","negative","log","posterior",",","for","a","model","with","a","covariance","matrix","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent_variables","Returns","--","--","--","--","--","Negative","log","posterior"]
code: def multivariate_neg_logposterior(self, beta):
      """Returns negative log posterior, for a model with a covariance matrix
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent_variables
  
      Returns
      ----------
      Negative log posterior
      """
  
      post = self.neg_loglik(beta)
      for k in range(0, self.z_no):
          if (
              self.latent_variables.z_list[k].prior.covariance_prior
              is True
          ):
              post += -self.latent_variables.z_list[k].prior.logpdf(
                  self.custom_covariance(beta)
              )
              break
          else:
              post += -self.latent_variables.z_list[k].prior.logpdf(
                  beta[k]
              )
      return post
  
imports: from math import exp, sqrt, log, tanh
  import copy
  import sys
  import warnings
  import numpy
  from scipy import optimize
  import numdifftools
  import pandas
  from .covariances import acf
  from .families import Normal
  from .inference import BBVI, BBVIM, MetropolisHastings, norm_post_sim
  from .output import TablePrinter
  from .tests import find_p_value
  from .latent_variables import LatentVariable, LatentVariables
  from .results import BBVIResults, MLEResults, LaplaceResults, MCMCResults
label_desc: Use image node_id 11 for calling the TSM obj's underlying member method code with example usage: obj.multivariate_neg_logposterior(beta) and returns: post