# Tag Info

35

Hint: The probability that an equal number of tails and heads appear is $\large{{2k \choose k} \frac{1}{2^{2k}}}$ The two remaining outcomes (that there are more heads than tails or more tails than heads) are equally likely.

21

Given: $(X,Y)$ is Uniformly distributed on a disc of unit radius. Then, the joint distribution of $n$ such points, $((X_1,Y_1), ..., (X_n,Y_n))$, each drawn independently, will have joint pdf: $$f( (x_1,y_1), ..., (x_n,y_n) ) = \begin{cases}\pi^{-n}& (x_1^2 + y_1^2 < 1) & \text{ & } \cdots \text{ & }&(x_n^2 + y_n^2 < 1) \\ 0 ... 17 Here are some asymptotics for the first part: Claim For every fixed m, the random variable$$ S_n^m = T_{n}^m - \log n - (m-1) \log\log n+\log((m-1)!) $$converges in distribution to the "extreme value" distribution independent of m given by$$ F_{S_\infty}(s) = \exp\left(-\mathrm e^{-s}\right). $$Proof For frog j and t\ge 0, let ... 13 Hint: Fair coin \implies Probability of tails occurring more = probability of heads occurring more = p, say. Probability of exactly equal number of heads and tails =1-2p. Can you find this one? 12 I think the situation is similar to that in algebra. In elementary school, you learned that 1+1=2. It was kinda obvious, right? In rigorous advanced algebra, however, you first have to define “1”, “2”, “+” and then you must prove that 1+1=2. Similarly, probability theory at an undergraduate level uses some informal but intuitively sound notions ... 9 Looking at the final balls is the way to go, as you can regard drawing all 60 balls as choosing one of the equally probable permutation. Working from the back, you can also ignore a colour once it has been drawn. The probability that the last ball is blue and that the last green comes after the last red is \dfrac{30}{10+20+30}\times \dfrac{20}{10+20} ... 9 As the other respondents have already noted, probability is already defined as a ratio. But here is another thing to think about. Normally when you take the ratio of two quantities, the result is measured in the units of the ratio of the units. For example, if you travel 10 meters in 5 seconds, your average velocity is 10/5 meters per second. But ... 9 I will complement saz's answerby proving that L (to use saz's terminology) is a random variable. EDIT: A simpler and more powerful proof was given later by saz here (see his remark at the end). Introduction & proof outline 0 Following is a proof that the measure of the path of an n-dimensional (n \geq 1) Brownian motion is itself a measurable ... 8 Unfortunately, I'm not aware of an alternative proof (or a more detailed version of the proof by Mörters/Peres). So, instead of providing you with a source, I'll follow the lines of Paul Lévy; hopefully filling the gaps in his proof. Let (B(t))_{t \geq 0}=((B^1(t),B^2(t)))_{t \geq 0} be a two-dimensional Brownian motion and L := \lambda(B([0,1])). ... 8 Wouldn't this be needlessly complicating things? Stirling formula tells you that$$\Gamma(x+1/2)\sim(2\pi)^{1/2}(x+1/2)^{x}e^{-x-1/2}$$and that$$ x^{1/2}\,\Gamma(x)\sim(2\pi)^{1/2}x^{x}e^{-x} $$hence$$ ...

8

There is a very elegant theorem published by Beardwood, Halton and Hammersley in 1959 that gives an alsmost-sure convergence for the length of the shortest path divided by $\sqrt{N}$. See this poster for a statement in a general setting. Also, the convergence holds for expectations. In 1989, Rhee and Talagrand showed that the length of the shortest path is ...

8

No, because you're not in a situation where you always remember two other wrong answers at the moment you're about to mark your choice. The analysis in the Monty Hall problem that leads to "it is an advantage to switch" depends on the fundamental assumption that you will be shown a non-prize door that you haven't chosen no matter which choice you made ...

8

For a Markov process $(X_t)_{t \geq 0}$ we define the generator $A$ by $$Af(x) := \lim_{t \downarrow 0} \frac{\mathbb{E}^x(f(X_t))-f(x)}{t} = \lim_{t \downarrow 0} \frac{P_tf(x)-f(x)}{t}$$ whenever the limit exists in $(C_{\infty},\|\cdot\|_{\infty})$. Here $P_tf(x) := \mathbb{E}^xf(X_t)$ denotes the semigroup of $(X_t)_{t \geq 0}$. By Taylor's formula ...

8

There is a difference between mutually exclusive events and independent events (indeed, a very strict difference in the sense that a couple of non-trivial events cannot be both mutually exclusive and independent). Events $A$ and $B$ are mutually exclusive if it is impossible for both of them to occur simultaneously. To borrow from your example, it is ...

8

Let us formulate your problem mathematically. Let $X_n$ be the outcome of each coin toss. $P(X_n = 1) = 1- p$ and $P(X_n = -1) = p$ Let $H_{n}$ be your bet size on $n$-th toss and this depends on outcomes of previous coin tosses $X_1,... X_n$. Let $S_n = \sum_{i=1}^n X_n$ Then your final wealth is $W_n = W_0+\sum_{i=1}^n H_n X_n = W_0 + \sum_{i=1}^n H_n ... 8 Then can you help me to show that$\alpha$is in fact stopping time? Well, not very easily I am afraid, since$\alpha$is not always a stopping time for the filtration$(\mathcal F_t)_{t\geqslant0}$defined by$\mathcal F_t=\sigma(X_s;0\leqslant s\leqslant t)$. For a counterexample, consider some Bernoulli random variable$U$such that$P(U=1)=\frac12$... 8 Note that the integral can be written as $$\int_0^\infty \int_x^\infty f(t)\,\mathrm dt\,\mathrm dx=\int_0^\infty \int_0^\infty f(t)\mathbf{1}_{t\geq x}\,\mathrm dt\,\mathrm dx$$ and using Fubini we get $$\int_0^\infty \int_0^\infty f(t)\mathbf{1}_{t\geq x}\,\mathrm dx\,\mathrm dt=\int_0^\infty \int_0^t f(t)\,\mathrm dx\,\mathrm dt.$$ Here ... 7 Let$(\Omega,\mathcal{F},P)$be a probability space, i.e.$\Omega$is a non-empty set,$\mathcal{F}$is a sigma-algebra of subsets of$\Omega$and$P:\mathcal{F}\to [0,1]$is a probability measure on$\mathcal{F}$. Now, suppose we have a function$X:\Omega\to\mathbb{R}$and we want to "measure" the probability of$X$belonging to some subset of$\mathbb{R}$. ... 7 You may assume the first point$A$at$(1,0)$and the second point$B=(\cos\phi,\sin\phi)$being uniformly distributed on the circle. The probability measure is then given by${1\over2\pi}{\rm d}\phi$. The distance$D:=|AB|$computes to$2\left|\sin{\phi\over2}\right|$, and we obtain $${\mathbb E}(D)={1\over 2\pi}\int_{-\pi}^\pi ... 7 Measure theory gives a unified mathematical and conceptual framework for general probability theory. The two classic scenarios in probability theory are the discrete and continuous measures, which are treated quite separately: In the discrete case, we have a finite or countable space \Omega and assign a probability p(x) to each point x \in \Omega, ... 7 Your final expression:$$a_k = \frac{1}{5} \cdot\left( \frac{25}{36} \right)^k $$This correctly gives the probability that your first 6 will occur on the 2kth roll. So, a_1 is the probability that your first 6 is on roll 2; a_2 is the probability that your first 6 is on roll 4; etc. In order to find the probability p that your first 6 is on any ... 7 Integrate the LHS and the RHS of the pointwise identity$$ X=\int_0^X\mathrm dx=\int_0^\infty\mathbf 1_{X\geqslant x}\,\mathrm dx. $$This shows that the desired formula for E[X] holds irrespectively of the hypothesis that X is discrete or continuous or neither discrete nor continuous, as soon as X\geqslant0 almost surely, and that two formulas are ... 7 The measure P_X (not P_x) is the image measure of P by X, defined by the identity P_X(B)=P(X^{-1}(B)) for every B in \mathcal B(\mathbb R) (and X being a random variable is exactly the hypothesis one needs to be sure that P(X^{-1}(B)) exists). The fact that, for every measurable function u suitably integrable,$$ \int_\Omega u(X)\,\mathrm ... 7 You want to look at the version of this question on MO. In his answer, Stefan Geschke indicates there that the axiom of choice is needed to exhibit examples, since any such finitely additive measure gives us sets of reals without the Baire property. (In a comment, Clinton Conley provides details.) The point is that there are models of set theory without the ... 7 Let throughout this post$(\Omega,\mathcal{F},P)$be a probability space, and let us first define the conditional expectation${\rm E}[X\mid\mathcal{G}]$for integrable random variables$X:\Omega\to\mathbb{R}$, i.e.$X\in L^1(P)$, and sub-sigma-algebras$\mathcal{G}\subseteq\mathcal{F}$. Definition: The conditional expectation${\rm E}[X\mid\mathcal{G}]$... 7 Watch the phrase "almost surely". Here is a written-out statement of the problem: Suppose that for each$n$, there exists an event$A_n$with$P(A_n) = 1$such that for every$\omega \in A_n$, we have$|X_n(\omega)| \le Y(\omega)$. Show that there exists an event$A$with$P(A) = 1$such that for every$\omega \in A$, we have$\sup_n |X_n(\omega)| \le ...

7

There is a Borel set $E$ in $\mathbb R^2$ such that $F := \{x-y\colon (x,y) \in E\}$ is not a Borel set. Let $A := \{f \in \mathbf{C}\colon (f(1), f(0)) \in E\}$. Then $A \in \mathcal{B}_{\left[0,\infty\right)}$. How about $T(A)$? In fact $$T(A) = \{g \in \mathbf{C}\colon g(0)=0, g(1) \in F\}$$ and is not Borel. added Mar 10 Why is $T(A)$ not Borel? ...

6

First, there are things that are much easier given the abstract formultion of measure theory. For example, let $X,Y$ be independent random variables and let $f:\mathbb{R}\to\mathbb{R}$ be a continuous function. Are $f\circ X$ and $f\circ Y$ independent random variables. The answer is utterly trivial in the measure theoretic formulation of probability, but ...

6

Let $f : \mathbb{R} \to \mathbb{R}$ be convex. This means that at every point $a \in \mathbb{R}$, there is an affine linear function $l_a : \mathbb{R} \to \mathbb{R}$ which is dominated by $f$, i.e. $$l_a(x) \leq f(x)$$ and $l_a(a) = f(a)$. When $f$ is differentiable, for example, then $l_a$ is the tangent to $f$ at $a$. When $f$ is strictly convex, we ...

6

Denote by $\Phi(t) = \mathbb{E}e^{\imath \, t \cdot X}$ the characteristic function of $X$. We have $$X = \frac{1}{2} \big((X+Y)+(X-Y) \big).$$ Thus, \begin{align*} \Phi(t) &= \mathbb{E}e^{\imath \, \frac{t}{2} (X+Y)} \cdot \mathbb{E}e^{\imath \, \frac{t}{2} (X-Y)}= \left( \mathbb{E}e^{\imath \, \frac{t}{2} X} \right)^2 \cdot \mathbb{E}e^{\imath \, ...

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