did two homeworks in math

2 files changed, 222 insertions, 0 deletions

diff --git a/houdre/hw4.tex b/houdre/hw4.tex
index d4ffebb..84dee63 100644
--- a/houdre/hw4.tex
+++ b/houdre/hw4.tex
@@ -24,19 +24,130 @@
 \def\qq{\qqq{\number\qnum}}
 \def\qqq#1{\bigskip\goodbreak\noindent{\bf#1)}\smallskip}
 \def\fr#1#2{{#1\over #2}}
+\def\var{\mathop{\rm var}\nolimits}
 
 \q5
 
+The generating function for the number of flowers is $$G_N(s) =
+\sum_{s=0}^\infty qp^ns^n = {q\over 1 - ps}.$$
+The probability generating function for each flower becoming a fruit is
+$x = {1+s\over2}$, and from theorem 4.36, $$G_S(s) = G_N(G_X(s)) =
+{q\over 1 - p{1+s\over2}} = {2q\over2-p}{1\over 1 - {p\over2-p}s}.$$
+This corresponds to $$\Pr(R=r) = {2q\over2-p}\left({p\over2-p}\right)^r.$$
 
+$$\Pr(N=n|R=r) = {\Pr(R=r|N=n)\over\Pr(R=r)}
+= {{n\choose r}(1/2)^r \over
+{2q\over2-p}\left({p\over2-p}\right)^r} =
+{(2-p)^{r+1}\over2q(2p)^r}{n\choose r}.$$
 
 \q6
 
+$\Pr(X=x) = {n\choose x}p^xq^{n-x}.$
+Its probability generating function $G_X$ is $$G_X(s) = (q+ps)^n.$$
+$$\E(X) = G_X'(1) = pn(q+ps)^{n-1} = pn.$$
+$$G_X''(1) = p^2n(n-1)(q+ps)^{n-2} = p^2n(n-1).$$
+$$\var(X) = G_X''(1) + G_X'(1) - G_X'(1)^2 = p^2n(n-1) + pn - p^2n^2
+= pn - p^2n = qpn$$
+
+Evenness can be determined as $(G_X(-1)+G_X(1))/2$, because $(-1)^s +
+1^s = 2$ iff s is even (else 0). These are in turn $G_X(-1) = (q-p)^n =
+(1-2p)^n$ and $G_X(1) = 1,$ giving $(1+(1-2p)^n)/2.$
+
+Divisibility by three is similar, but instead of $1$ and $-1$, the cubic
+roots of unity are used: $1,$ $e^{i2\pi/3},$ and $e^{i4\pi/3}.$ $G_X$ on
+these values will sum to $3$ for any number divisible by three because
+these numbers cubed is $1$ and sum to $0$ for other numbers.
+$(G_X(1) + G_X(e^{i2\pi/3}) + G_X(e^{i4\pi/3}))/3
+= (1 + (q+pe^{i2\pi/3})^n + (q+pe^{i4\pi/3})^n).%
+%(\cos(2\pi/3) + i\sin(2\pi/3)))^n + (q+p(\cos(2\pi
+$
+% yeah ig you have to do the simplification
+
 \q8
 
+For some value of $N$, $X$ and $Y$ follow binomial distributions.
+They are independent,
+
+Because $X$ and $Y$ are independent and $N=X+Y$, $$G_N(s) =
+G_X(s)G_Y(s).$$ For a given value of $N$, $X$ and $Y$ both follow
+binomial distributions, meaning, for a given value of $N=n$, $$G_X(s) =
+G_Y(s) = (\fr12[1+s])^n.$$ Conditioning on $N$ gives the more valid
+expressions $$G_X(s) = G_Y(s) = \sum_{i=0}^\infty
+\Pr(N=i)(\fr12[1+s])^i = G_N(\fr12[1+s]).$$
+Combining this with the first expression gives $$G_N(s) =
+G_N(\fr12[1+s])^2.$$ Let $H_N(s) = G_N(1-s)\to G_N(s) = H_N(1-s).$ This
+gives an identity $$H_N(1-s) = H_N(1-\fr12[1+s])^2 =
+H_N(\fr12[1-s])^2 \to H_N(s) = H_N(\fr12s)^2.$$
+There is only one function that fits this description: the exponential
+curve $H_N(s) = e^{-\lambda s} \to G_N(s) = e^{-\lambda(1-s)} =
+e^{\lambda(s-1)},$ corresponding uniquely to $\sum_{k=0}{1\over
+k!}\lambda^ke^{-\lambda}s^k.$ This is a Poisson distribution.
+
 \q9
 
+There is always a $p=1/3$ chance of finding a red token in each
+collection. The probability of a string of $j$ collections not having a
+red token in the first $j-1$ and a red token in the final collection is
+$p(1-p)^{j-1},$ so the generating function is $$\sum_{j=0}^\infty
+p(1-p)^{j-1}s^j = ps\sum_{j=0}^\infty (s(1-p))^{j-1} =
+{ps\over1-s(1-p)}.$$
+
+In the general case,
+let $G_{Y_n}$ be the time to acquire $n$ coupons. $G_{Y_1} = s$ because
+it always takes only one collection to acquire a unique coupon. After
+$n$ coupons have been collected, there is a $p = (m-n)/m$ chance of
+obtaining a new coupon, so the probability of ``time to next coupon''
+being $k$ is $pq^{k-1},$ giving a generating function $ps\over1-qs$
+after any given previous value. Convolving with $G_{Y_n}$ gives
+$$G_{Y_{n+1}}(s) = G_{Y_n}(s){{m-n\over m}s\over1-(n/m)s}
+\Longrightarrow G_{Y_n}(s) = {s^n\over m^n}{(m-1)(m-2)\cdots(m-n) \over
+(m-s)(m-2s)(m-3s)\cdots(m-ns)/m^n}.$$
+$$\Longrightarrow G_Y(s) = {s^m(m-1)!\over(m-s)(m-2s)\cdots(m-ms)}.$$
+$$\Longrightarrow \E(Y) = G_Y'(1) = m(m-1!)\cdot
+    {{1\over 1}+{1\over2}+\cdots+{1\over m}\over(m-1)!}.$$
+
+This simplifies to the given form.
+
 \q10
 
+The mean value of a discrete random variable $X$ is $\E(X) =
+\sum_{i\in X} i\Pr(X=i).$ For a nonnegative integer-valued random
+variable, its generating function is $\phi(s) = \sum_{i=0}^\infty
+\Pr(X=i)s^i.$
+
+$\phi'(1) = \sum_{i=0}^\infty i\Pr(X=i) = \E(X),$ so $\phi(s) =
+p(s)/q(s)$ has mean value $$\phi'(1) = {p'(1)\over q(1)} -
+{p(1)q'(1)\over q(1)^2} = {p'(1)\over q(1)} - \phi(1){q'(1)\over q(1)} =
+{p'(1)-q'(1)\over q(1)}$$
+because $\phi(1) = 1$ because it is a probability generating function.
+
+Duelist A wins the duel on their nth shot with probability $a$ for
+making the nth shot and $(1-a)^{n-1}(1-b)^{n-1}$ for it not already
+having been made. Let $r = (1-a)(1-b).$
+$$\sum_{i=0}^\infty ar^i = {a\over 1-r} = {a\over a+b-ab}$$
+This is the probability duelist A wins.
+
+The probability distribution of the number of shots fired is
+well-described by a probability generating function
+$\phi(s) = {a\over 1-rs^2} + {b(1-a)s\over 1-rs^2} = {a+b(1-a)s\over
+1-rs^2}.$ These represent, respectively, the series of chances A and B
+have to win the duel, so $$\E(\hbox{shots fired}) = \phi'(1) = {b(1-a) +
+2r\over 1-r}$$
+by the earlier established identity with $p=a+bs(1-a)$ and $q=1-rs^2.$
+This simplifies to $(2-b)(1-a)\over a+b-ab.$
+
 \q11
 
+With $N=n,$
+$$G_F(s) = \sum_{i=0}^n{n\choose i}p^i(1-p)^{n-i}s^i = (ps+1-p)^n.$$
+Conditioning on $N$ gives
+$$G_F(s) = \Pr(N=0) + \Pr(N=1)(ps+1-p) + \Pr(N=2)(ps+1-p)^2 +\cdots
+= G_N(ps+1-p).$$
+
+(b) is true by 3.6.14.
+
+(c) is a case of 4.5.8, where from $p=1/2$ (fair coin) and independence
+of two variables summing to a larger variable $N$ proves that the larger
+variable $N$ is a Poisson distribution.
+
 \bye
diff --git a/houdre/hw5.tex b/houdre/hw5.tex
new file mode 100644
index 0000000..a952e76
--- /dev/null
+++ b/houdre/hw5.tex
@@ -0,0 +1,111 @@
+\newfam\rsfs
+\newfam\bbold
+\def\scr#1{{\fam\rsfs #1}}
+\def\bb#1{{\fam\bbold #1}}
+\let\oldcal\cal
+\def\cal#1{{\oldcal #1}}
+\font\rsfsten=rsfs10
+\font\rsfssev=rsfs7
+\font\rsfsfiv=rsfs5
+\textfont\rsfs=\rsfsten
+\scriptfont\rsfs=\rsfssev
+\scriptscriptfont\rsfs=\rsfsfiv
+\font\bbten=msbm10
+\font\bbsev=msbm7
+\font\bbfiv=msbm5
+\textfont\bbold=\bbten
+\scriptfont\bbold=\bbsev
+\scriptscriptfont\bbold=\bbfiv
+
+\def\Pr{\bb P}
+\def\E{\bb E}
+\newcount\qnum
+\def\q{\afterassignment\qq\qnum=}
+\def\qq{\qqq{\number\qnum}}
+\def\qqq#1{\bigskip\goodbreak\noindent{\bf#1)}\smallskip}
+\def\fr#1#2{{#1\over #2}}
+\def\var{\mathop{\rm var}\nolimits}
+\def\infint{\int_{-\infty}^\infty}
+
+\q1
+
+The mean of this distribution $\E(X)$ is
+$$\E(X) = \infint xf(x) dx.$$
+This integral evaluates to 0 by symmetry because $f(x) =
+{1\over2}ce^{-c|x|}$ is an even function, so $xf(x)$ is odd.
+
+The variance of this distribution is $\var(X) = \E(X^2) - \E(X)^2 =
+\E(X^2).$ By theorem 5.58,
+$$\E(X^2) = \infint x^2f(x) dx = \infint x^2{1\over2}ce^{-c|x|} dx
+= \int_0^\infty x^2ce^{-cx} dx
+= ce^0{2\over c^3} = 2c^{-2}.$$
+by repeated integration by parts
+
+\q2
+
+$$\Pr(X\geq w) = \sum_{k=w}^\infty {1\over k!}\lambda^ke^{-\lambda}.$$
+$$\Pr(Y\leq \lambda) = \int_0^\lambda {1\over\Gamma(w)} x^{w-1}e^{-x}dx
+= \int_0^\lambda {x^{w-1}e^{-x}\over (w-1)!} dx,$$
+because $\Gamma(w) = (w-1)!$
+
+We are going to prove this equality by induction on $w$.
+It is true for $w=1$ because the Poisson distribution will sum to
+$1-e^{-\lambda}$ (because $\Pr(X\geq0) = 1$ and $\Pr(X<1) = \Pr(X=0) =
+e^{-\lambda}\lambda^0/0!.$)
+The Gamma distribution is ${1\over\Gamma(1)}\int_0^\lambda x^0e^{-x}dx =
+-e^{-x}\big|^\lambda_0 = -e^{-\lambda} + 1.$
+
+Assuming the following equality holds for $w,$ it will be shown to
+hold for $w+1$:
+$$\sum_{k=w}^\infty {1\over k!}\lambda^ke^{-\lambda} = \int_0^\lambda
+{x^{w-1}e^{-x}\over (w-1)!} dx.$$
+$$\sum_{k=w}^\infty {1\over k!}\lambda^ke^{-\lambda}
+= {1\over w!}\lambda^we^{-\lambda} + \sum_{k=w+1}^\infty
+{\lambda^ke^{-\lambda}\over k!}.$$
+$${1\over (w-1)!}\int_0^\lambda x^{w-1}e^{-x}dx
+= {1\over (w-1)!}\left({x^we^{-x}\over w}\big|^\lambda_0 +
+\int_0^\lambda {x^we^{-x}\over w} dx\right) =
+{\lambda^we^{-\lambda}\over w!} + \int_0^\lambda {x^we^{-x}\over
+\Gamma(w+1)} dx$$
+by integration by parts.
+$$\Longrightarrow \sum_{k=n+1}^\infty {\lambda^ke^{-\lambda}\over k!} =
+\int_0^\lambda {x^we^{-x}\over \Gamma(w+1)} dx.$$
+
+QED
+
+\q3
+
+The density function $f(x)$ is proportional, so there is some constant
+$c$ such that $$1 = f(x) = c\infint g(x)dx = 2c\int_1^\infty x^{-n}dx =
+2c\left(-{x^{1-n}\over n-1}\right)\big|^\infty_1 = 2c\left({1\over
+n-1}\right) \to c = {n-1\over2}.$$
+
+The mean and variance of $X$ exist when $\E(X)$ and $\E(X^2)$ exist,
+respectively. The first exists when $n>2$ because for $n=2,$
+$$\E(X) = \int_1^\infty xx^{-n}dx = \ln(x)\big|^\infty_1,$$ and,
+similarly for $\E(X^2)$ under $n\leq3.$ $n>3$ is the condition that it
+exists because $$\int_1^\infty x^2x^{-n}dx = \ln(x)\big|^\infty_1,$$ for
+$n=3$ (and $\int\ln(x)dx$ for $n=2$).
+
+\q4
+
+The density function of $Y=|X|$ is:
+$$\{x\geq0: {2\over\sqrt{2\pi}}\exp(-{1\over2}x^2).\hbox{ 0
+otherwise.}\}$$
+
+$$\E(Y) = \int_0^\infty {2x\over \sqrt{2\pi}}\exp(-{1\over2}x^2)dx =
+{1\over\sqrt{2\pi}}\int_0^\infty\exp(-\fr u2)du,$$
+by u-substitution, becoming
+$$-{2\over\sqrt{2\pi}}e^{-u\over2}\big|_0^\infty =
+{\sqrt{2}\over\sqrt{\pi}}.$$
+
+Similarly, $\var(x) = \E(Y^2) - \E(Y)^2.$
+$$\E(Y^2) = \int_0^\infty {2x^2\over \sqrt{2\pi}}\exp(-\fr12 x^2)dx
+= -{2\over\sqrt{2\pi}}x\exp(-\fr12 x^2)\big|^\infty_0
+- {2\over\sqrt{2\pi}}\int_0^\infty -e^{-\fr12 x^2}dx,$$
+by integration by parts, turning into
+$$0 + 1,$$
+because the first evaluates to zero at both extrema and the second is
+the distribution function of the normal distribution so integrates to 1.
+
+\bye