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For 2 N =1000 sample 10,000 realisations of each of the random variables respectively. Display a normalized histogram for all three simulations, along with the

For 2N=1000 sample 10,000 realisations of each of the random variables respectively. Display a normalized histogram for all three simulations, along with the probability density function of the arcsine distribution, to check the above facts numerically!

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File Edit View Insert Cell Kernel Widgets Help Not Trusted | Python 3 O I+KE+WHRunICt>Code v- The purpose of this python homework is to explore the socalled Arcsine laws numerically. The arcsine laws are a number of fascinating results for random walks. They relate path properties of the simple symmetric random walk to the arcsine distribution. A random variable X on [0, l] is arcsine-distributed if the cumulative distribution function is given by IP[X S x] = gaming/J?) for all 0 S x S 1 and the probability density function is given by l fX(x) = W on (0, 1). Given a simple symmetric random walk ($0,120 with So = 0, we define the following random variables: - The total number of periods from 0 to 2N the random walk spends above zero: CZN1=|{n E {1,... ,2N} :S,l > 0}|. - The time of the last visit to 0 before time 2N : LIN := max{0 S n S 2N : 5,, = 0}. - The time when the random walk reaches its unique maximum value between time 0 and 2N: M2\" := argmax{S,, : 0 S n S 2N} (this notation means that SMZN = max{S,, : 0 S n S 2N}. As usual, we start with loading some packages: File Edit View Insert Cell Kernel Widgets Help i Not Trusted | Python 3 O i||+isoo 2N . MN 2 . _ oo We say that the random variables CZN/ZN, Lani/2N, MzN/ZN converge in distribution to the Arcsine Distribution. The interesting property about the Arcsine distribution is that its density (see its formula above) is U-shaped on (0, 1). In other words, if X is arcsine-distributed on (0, 1), the probabilty that X takes very small values near 0 or very large values near 1 is rather high, but the probability for taking values around, say, 0.5, is low. H In [11]: x = np. linspace(arcsine.ppf(0. 05), arcsine. ppf(0. 95), 100) i plt. title (\"Density of the arcsine distribution\") p1t.plot (x, arcsine.pdf(x), linewidth=2, color='b') Out[11]: [] Density of the arcsine distribution 41] 3.5 10 15 File Edit View insert Cell Kernel Widgets Help Not Trusted | Python 3 O ilii+iia

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