diff --git a/factorOperations.py b/factorOperations.py
index e96250b..304d7b7 100644
--- a/factorOperations.py
+++ b/factorOperations.py
@@ -102,7 +102,40 @@ def joinFactors(factors: List[Factor]):
 
 
     "*** YOUR CODE HERE ***"
-    raiseNotDefined()
+    conditioned_variables = set()
+    unconditioned_variables = set()
+
+
+    for factor in factors:
+        print(factor)
+        '''
+        print(factor.unconditionedVariables())
+        print(factor.conditionedVariables())
+        '''
+        conditioned_variables.update([ i for i in factor.conditionedVariables()])
+        unconditioned_variables.update([ i for i in factor.unconditionedVariables()])
+
+    # remove unconditioned_variables from new conditioned_variables
+    for var in list(conditioned_variables):
+        if var in unconditioned_variables:
+            conditioned_variables.remove(var)
+
+    # construct the new factor
+    # then, we should change the probability.
+
+    # Hint from CS188's page:
+    #     For this problem, you may assume that all the input Factors have come from the same BayesNet, and so their variableDomainsDicts are all the same
+    new_factor = Factor(unconditioned_variables, conditioned_variables, list(factors)[0].variableDomainsDict())
+
+    # new probability
+    for assignment in new_factor.getAllPossibleAssignmentDicts():
+        prob = 1
+        for factor in factors:
+            prob *= factor.getProbability(assignment)
+        new_factor.setProbability(assignment, prob)
+
+    return new_factor
+
     "*** END YOUR CODE HERE ***"
 
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