// Numbas version: finer_feedback_settings
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Consider a dice being thrown an infinite number of times, can the following be modeled as a Markov chain?
", "advice": "Both $U_n$ and $V_n$ possess the Markov property. If we know the value of $U_n$ or $V_n$, it tells us everything we need to know about the probabilities of being in a particular state at time point $n+1$.
\nThe limiting distribution refers to what proportion of time we spend in each state in the long run. It is clear for the $U_n$ process that eventually we will end up in state 6 and then remain there forever, so it is easy to write down the limiting distribution without any calculations. When it is not obvious, the limiting distribution (when it exists) can be found by finding the unique stationary distribution with $\\pi^T\\boldsymbol{P}=\\pi^T$ subject to the sum of the entries of $\\pi$ being 1, where $\\boldsymbol{P}$ is the transition probability matrix.
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", "minMarks": 0, "maxMarks": 0, "shuffleChoices": true, "displayType": "radiogroup", "displayColumns": 0, "showCellAnswerState": true, "choices": ["True", "False"], "matrix": ["1", 0], "distractors": ["", ""]}, {"type": "gapfill", "useCustomName": false, "customName": "", "marks": 0, "scripts": {}, "customMarkingAlgorithm": "", "extendBaseMarkingAlgorithm": true, "unitTests": [], "showCorrectAnswer": true, "showFeedbackIcon": true, "variableReplacements": [], "variableReplacementStrategy": "originalfirst", "nextParts": [], "suggestGoingBack": false, "adaptiveMarkingPenalty": 0, "exploreObjective": null, "prompt": "Fill in the transitional probability matrix for $U_n$
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