Plagiarism and collusion

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1.) Empirical distribution [10 points]

  • Let \(X_1, \ldots, X_n\) be i.i.d. random variables all with cdf \(F\). Show that for every \(x \in \mathbb{R}\), we have

\[ \frac{1}{n} \sum_{i=1}^n \mathbf{1}[ X_i \leq x] \to F(x). \ \text{[5 points]}\]

  • Demonstrate this fact by running simulations of a large number of random variables that are uniformly distributed in the unit interval \([0,1]\). [5 points]

2.) Generating random variables [10 points]

  • Show that if \(X\) is a continuous random variable taking values on \(D\) with a cdf that is strictly increasing on \(D\), then the random variable \(F(X)\) is uniformly distributed on the unit interval \([0,1]\). [3 points]

  • Show that if \(U\) is uniformly distributed in \([0, \tfrac{\pi}{2}]\), then \(\sin^2(U)\) has the beta distribution with parameters \((\tfrac{1}{2}, \tfrac{1}{2})\). [3 points]

  • Suppose that you have access to a true source of randomization given by say radioactive decay; that is, you have access to independent random variables that are exponentially distributed with rate \(1\). Show that you can generate random variables with the beta distribution with parameters \((\tfrac{1}{2}, \tfrac{1}{2})\). [2 points]

  • Demonstrate your procedure in the last question, by computer simulations, and plot a histogram of the results against pdf of the beta \((\tfrac{1}{2}, \tfrac{1}{2})\). [2 points]

3.) Random walk [5 points]

Let \(S_n = X_1 + \cdots + X_n\), where \(X_i\) are i.i.d. random variables, with \[\mathbb{P}(X_1 = 1) = \tfrac{1}{2} = \mathbb{P}(X_1 = -1).\]

Let \[L_n = \# \{ 1 \leq k \leq n : S_k >0\}.\] Demonstrate, by simulations, that \(L_n/n\) converges in distribution to the beta \((\tfrac{1}{2}, \tfrac{1}{2})\) distribution.

4.) Exporting and importing data [5 points]

  • Simulate \(250\) random variables that are uniformly distributed in \([0,1]\).
  • Export them to a tab delimited text file named export.txt.
  • Now import them back and save them under the variable imported.
  • Plot a probability histogram of the imported data. You may need to do some processing as the values may be in a table, rather than a vector.

5.) Estimating the stationary distribution [10 points]

  • Suppose you are given the output of a \(100000\) steps of a irreducible and aperiodic finite state Markov chain. Carefully explain how you could estimate the stationary distribution for this Markov chain, and why you estimator is reasonable. [5 points]

  • Import the data from the file markovchain.txt and use this data and your method above to estimate the stationary distribution. [5 points]

6.) Poisson processes [10 points]

Suppose a shop that operates daily in the time interval \([a,b]\). It has customers arriving according to a Poisson process of intensity \(3\) in the time interval \([a, c)\), and a Poisson process of intensity \(5\) in the time interval \([c,b)\); here \(a\) and \(b\) are known, but \(c\) is unknown. You can imagine the shop keeper notices that at some point in the day, the shop seems to get busier. The shop keeper has a log of all the arrival times, for each of \(n\) days of operation, where \(n\) is large.

  • Given an open interval \((r,s) \subset [a,b]\), explain how you can use the shop keeper’s log to make a good guess at whether or not \((r,s)\) contains the unknown time \(c\); show that as \(n \to \infty\) you will know with certainty whether \(c \in (r,s)\). Carefully explain your answer. [5 points]

  • Demonstrate your answer by running simulations; for example, choose \(a=0\), \(b=8\), and \(c=4\), and simulate the arrivals to generate the shop keeper’s log. Now apply your method with the intervals \((2.7, 4.3)\) and \((5,6)\). [5 points]