class_docstr: Inherits time series methods from TSM class.
  
  **** NON-GAUSSIAN LOCAL LEVEL MODEL ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many times to difference the time series.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  family : 
      e.g. pf.Normal(0,1)
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","NON-GAUSSIAN","LOCAL","LEVEL","MODEL","*","*","*","*","Parameters","--","--","--","--","--","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","time","series","data","that","will","be","used",".","integ",":","int","(","default",":","0",")","Specifies","how","many","times","to","difference","the","time","series",".","target",":","str","(","pd.DataFrame",")","or","int","(","np.array",")","Specifies","which","column","name","or","array","index","to","use",".","By","default",",","first","column\/array","will","be","selected","as","the","dependent","variable",".","family",":","e.g",".","pf.Normal","(","0,1",")"]
docstr: Creates simplest kind of approximating Gaussian model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  T, Z, R, Q : np.array
      State space matrices used in KFS algorithm
  
  h_approx : float
      Value to use for the H matrix
  
  Returns
  ----------
  
  H : np.array
      Approximating measurement variance matrix
  
  mu : np.array
      Approximating measurement constants
docstr_tok: ["Creates","simplest","kind","of","approximating","Gaussian","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","T",",","Z",",","R",",","Q",":","np.array","State","space","matrices","used","in","KFS","algorithm","h_approx",":","float","Value","to","use","for","the","H","matrix","Returns","--","--","--","--","--","H",":","np.array","Approximating","measurement","variance","matrix","mu",":","np.array","Approximating","measurement","constants"]
code: def _general_approximating_model(self, beta, T, Z, R, Q, h_approx):
      """Creates simplest kind of approximating Gaussian model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      T, Z, R, Q : np.array
          State space matrices used in KFS algorithm
  
      h_approx : float
          Value to use for the H matrix
  
      Returns
      ----------
  
      H : np.array
          Approximating measurement variance matrix
  
      mu : np.array
          Approximating measurement constants
      """
  
      H = np.ones(self.data_length) * h_approx
      mu = np.zeros(self.data_length)
  
      return H, mu
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  from scipy import optimize
  from ..None import inference
  from ..None import families
  from ..None import output
  from ..None import tsm
  from ..None import data_check
  from ..None import covariances
  from ..None import results
  from ..None import gas
  from .kalman import *
  from .llm import *
label_desc: Use image node_id 16 for calling the NLLEV obj's underlying member method code with example usage: obj._general_approximating_model(beta, T, Z, R, Q, h_approx) and returns: H,  mu