class_docstr: Class to represent a Markov Chain with multiple kernels for factored state space,
  along with methods to simulate a run.
  
  Examples
  --------
  
  Create an empty Markov Chain:
  
  >>> from pgmpy.models import MarkovChain as MC
  >>> model = MC()
  
  And then add variables to it
  
  >>> model.add_variables_from(['intel', 'diff'], [2, 3])
  
  Or directly create a Markov Chain from a list of variables and their cardinalities
  
  >>> model = MC(['intel', 'diff'], [2, 3])
  
  Add transition models
  
  >>> intel_tm = {0: {0: 0.25, 1: 0.75}, 1: {0: 0.5, 1: 0.5}}
  >>> model.add_transition_model('intel', intel_tm)
  >>> diff_tm = {0: {0: 0.1, 1: 0.5, 2: 0.4}, 1: {0: 0.2, 1: 0.2, 2: 0.6 }, 2: {0: 0.7, 1: 0.15, 2: 0.15}}
  >>> model.add_transition_model('diff', diff_tm)
  
  Set a start state
  
  >>> from pgmpy.factors.discrete import State
  >>> model.set_start_state([State('intel', 0), State('diff', 2)])
  
  Sample from it
  
  >>> model.sample(size=5)
     intel  diff
  0      0     2
  1      1     0
  2      0     1
  3      1     0
  4      0     2
class_docstr_tok: ["Class","to","represent","a","Markov","Chain","with","multiple","kernels","for","factored","state","space",",","along","with","methods","to","simulate","a","run",".","Examples","--","--","--","--","Create","an","empty","Markov","Chain",":",">",">",">","from","pgmpy.models","import","MarkovChain","as","MC",">",">",">","model","=","MC","(",")","And","then","add","variables","to","it",">",">",">","model.add_variables_from","(","[","'intel","'",",","'diff","'","]",",","[","2",",","3","]",")","Or","directly","create","a","Markov","Chain","from","a","list","of","variables","and","their","cardinalities",">",">",">","model","=","MC","(","[","'intel","'",",","'diff","'","]",",","[","2",",","3","]",")","Add","transition","models",">",">",">","intel_tm","=","{","0",":","{","0",":","0.25",",","1",":","0.75","}",",","1",":","{","0",":","0.5",",","1",":","0.5","}","}",">",">",">","model.add_transition_model","(","'intel","'",",","intel_tm",")",">",">",">","diff_tm","=","{","0",":","{","0",":","0.1",",","1",":","0.5",",","2",":","0.4","}",",","1",":","{","0",":","0.2",",","1",":","0.2",",","2",":","0.6","}",",","2",":","{","0",":","0.7",",","1",":","0.15",",","2",":","0.15","}","}",">",">",">","model.add_transition_model","(","'diff","'",",","diff_tm",")","Set","a","start","state",">",">",">","from","pgmpy.factors.discrete","import","State",">",">",">","model.set_start_state","(","[","State","(","'intel","'",",","0",")",",","State","(","'diff","'",",","2",")","]",")","Sample","from","it",">",">",">","model.sample","(","size=5",")","intel","diff","0","0","2","1","1","0","2","0","1","3","1","0","4","0","2"]
docstr: Checks if a list representing the state of the variables is valid.
docstr_tok: ["Checks","if","a","list","representing","the","state","of","the","variables","is","valid","."]
code: def _check_state(self, state):
      """
      Checks if a list representing the state of the variables is valid.
      """
      if not hasattr(state, "__iter__") or isinstance(state, str):
          raise ValueError(
              "Start state must be a non-string iterable object."
          )
      state_vars = {s.var for s in state}
      if not state_vars == set(self.variables):
          raise ValueError(
              f"Start state must represent a complete assignment to all variables."
              f"Expected variables in state: {state_vars}, Got: {set(self.variables)}."
          )
      for var, val in state:
          if val >= self.cardinalities[var]:
              raise ValueError(f"Assignment {val} to {var} invalid.")
      return True
  
imports: from collections import defaultdict
  import numpy
  from pandas import DataFrame
  from scipy.linalg import eig
  from pgmpy.factors.discrete import State
  from pgmpy.global_vars import logger
  from pgmpy.utils import sample_discrete
label_desc: Use image node_id 3 for calling the MarkovChain obj's underlying member method code with example usage: obj._check_state(state) and returns: True