• To see that Item 2. \implies item 1. we use the monotone class theorem. Fix B \in \mathcal{D} then the collection \left\{ A \in \mathcal{E}\,:\, P(X \in A, Y \in B)=P(X\in A) P(Y \in B)\right\} contains the p-system \mathcal{C} and is a d-system (contains \Omega, closed under complement, closed under increasing limits, check this yourself please). Therefore by the monotone class theorem it contains \mathcal{E}. Analgously fix now A \in \mathcal{E}, then the set
  \left\{ B \in \mathcal{F}\,:\, P(X \in A, Y \in B)=P(X\in A) P(Y \in B)\right\} contains \mathcal{F}.

• To see that item 3. \implies item 1. take f(X)=1_A(X) and g(Y)=1_{B}(Y). If these two random variable are independent, this simply means that the event X\inA and Y\in B are independent. Conversely we note that V=f(X) is measurable with respect to \sigma(X): since V^{-1}(B) = X^{-1} (f^{-1}(B)) \in \sigma(X) this shows that \sigma(f(X))\subset \sigma(X). Likewise \sigma(g(Y)) \subset \sigma(Y).
  Since \sigma(X) and \sigma(Y) are independent so are \sigma(f(X)) and \sigma(g(Y)).

• To see that item 4. \implies item 1. take f=1_A and g=1_B. To show that item 1. \implies item 4. note that item 1. can be rewritten that E[1_A(X) 1_B(Y)] =E[1_A(X)] E[1_B(Y)]. By linearity of the expectation then item 4. holds for all simple functions g and g. If f and g are non negative then we choose sequences of simple functions such that f_n \nearrow f anf g_n \nearrow g. We have then f_n g_n \nearrow fg and using the monotone convergence theorem twice we have \begin{aligned} E[f(X)g(Y)] & = E[ \lim_{n} f_n(X) g_n(Y)] = \lim_{n} E[ f_n(X) g_n(Y)] = \lim_{n} E[ f_n(X) ] E[g_n(Y)] \\ &= \lim_{n} E[ f_n(X) ] \lim_{n} E[g_n(Y)] =E[ f(X) ] E[g(Y)] \end{aligned}

Applying MCT again to the function g_n(x) = \sum_{k=1}^n Q(C_n(x)) we find that \sum_{n=1}^\infty R(C_n) = \sum_{n=1}^\infty \int_E Q(C_n(x)) dP(x) = \int_E \sum_{n=1}^\infty Q(C_n(x)) dP(x) = \int_E Q(C(x)) dP(x) =R(C). and this shows that R is a probability measure. Uniqueness of R follows from the monotone class theorem.

For item 2. note that in item 1, we have proved it in the case where f(x,y)= 1_C(x,y). By linearity the result then holds for simple functions. If f is nonnegative and measurable then pick an increasing sequence such that f_n \nearrow f. Then by MCT \int f(x,y) d P\otimes Q(x,y) = \lim_{n} \int f_n(x,y) d P\otimes Q(x,y) = \lim_{n} \int_E (\int_F f_n(x,y) d Q(y)) dP(x) But the function x \to \int_F f_n(x,y) d Q(y) is increasing in n and by MCT again, and again. \begin{aligned} \int f(x,y) d P\otimes Q(x,y) & = \int_E \lim_{n} (\int_F f_n(x,y) d Q(y)) dP(x) = \int_E (\int_F \lim_{n} f_n(x,y) d Q(y)) dP(x) \\ &= \int_E (\int_F f(x,y) d Q(y)) dP(x) \end{aligned} Simlarly one shows that \int f(x,y) d P\otimes Q(x,y) = \int_F (\int_E f(x,y) d P(x)) dQ(y). The result for integrable f follows by decomposing into postive and negative part. \quad \square

Applying this to random variables we get

Fubini-Tonelli for countably many RV:

We extend this result to countable many independent random variables: this is important in practice where we need such models: for example flipping a coin as many times as needed!

This can be seen as an extension of Fubini theorem and is also a specical case of the so-called Kolmogorov extension theorem which us used to construct general probability measures on infinite product space. No proof is given here.

Infinite product \sigma-algebras
Given \sigma-algebras \mathcal{A}_j on \Omega_j we set \Omega = \prod_{j=1}^\infty \Omega_j and define rectangles for n_1 < n_2 < \cdots < n_k and k finite but arbitrary A_{n_1} \times A_{n_2} \times \cdots \times A_{n_k} = \{ \omega=(\omega_1,\omega_2,\omega_3, \cdots) \in \Omega \;: w_{n_j} \in A_{n_j}\} where A_{n_j} \in \mathcal{A_{n_j}}. The product \sigma-algebra \mathcal{A} = \bigotimes_{j=1}^\infty \mathcal{A}_j the \sigma-algebras generated by all the rectangles.

If we have a RV X_n: \Omega_n \to \mathbb{R} then we define its extension to \Omega by \tilde{X_n}(\omega) = X_n(\omega_n) and the distribution of \tilde{X_n} is the same as the distribution of X_n because \tilde{X_n}^{-1}(B_n) = \Omega_1 \times \cdots \times \Omega_{n-1} \times X_n^{-1}(B_n) \times \Omega_{n+1} \times \cdots and thus P(\tilde{X_n}\in B_n) =P_n (X_n \in B_n)\,. A similar computation shows that \tilde{X}_n and \tilde{X}_m are independent for n \not= m or more generally any finite collection of X_j's are independent.

Examples Given independent random variable X_1, X_2, \cdots we define S_n = X_1 + X_2 +\cdots + X_n.

• The event \{ \omega\,:\, \lim_{n} X_n(\omega) \text{ exists }\} belongs to every \mathcal{C}_n and thus belong to \mathcal{C}_\infty. Therefore X_n either converges a.s or diverges a.s.

• A random variable which is measurable with respect to \mathcal{C}_\infty must be constant almost surely. Therefore \limsup_n X_n \,, \quad \liminf_n X_n\,, \quad \limsup_n \frac{1}{n} S_n\,, \quad \liminf_n \frac{1}{n} S_n are all constant almost surely.

• The event \limsup_{n} \{ X_n \in B\} (also called \{ X_n \in B \text{ infinitely often }\}) is in \mathcal{C}_\infty.

• The event \limsup_{n} \{ S_n \in B\} is not in \mathcal{C}_\infty.

The following theorem is a generalization of Fubini-Tonelli

• This theorem is intimately related to the concepts of conditional probability and conditional expectations which we will study later.

• Roughly speaking, on nice probability space (e.g. separable metric spaces), every probaility measure on the product space E \times F can be constructed in the way described in Theorem 2.1 (this is a deep result).

• If R=P \otimes Q is a product measure then the marginal of R are P and Q. This is for the kernel K(x,A)=Q(A).

• If R(dx,dy)= P(dx) K(x,dy) then we have R(A \times F) = \int_A K(x,F) dP(x) = P(A) \quad \quad R(E \times B) = \int_E K(x,B) dP(X) so its marginals are P and and Q given by Q(B) = \int_E K(x,B) dP(x).

By doing a Fubini-Tonelli theorem type argument one can construct the Lebesgue measure on \mathbb{R}^n.

Notations we often use the notation dx or dx_1 \cdots dx_n for integration with respect to m_n.

To prove item 3, we have \int k(x,y) dy = \int \frac{f(x,y)}{f_X(x)} dy = \frac{f_X(x)}{f_X(x)} =1 and thus k(x,y) is a density. Furthermore we have \begin{aligned} P( X \in A, Y \in B) &= \int_A K(x,B) dP(x) = \int_A f_X(x) \left(\int_B k(x,y) dy \right) dx = \int_A f_X(x) \left(\int_B \frac{f(x,y)}{f_X(x)} dy \right) dx \\ & = \int_A \int_B f(x,y) dx dy \end{aligned} and this concludes the proof since the measure is uniquely determined by its value on rectangles (by a monotone class theorem arguemnt).

We have then for any nonnegative h \begin{aligned} E[h(Z,U)]&=E\left[h\left((X_1+X_2,\frac{X_1}{X_1+X_2}\right)\right]\\ &= \int_0^\infty \int_0^\infty h\left(x_1+x_2,\frac{x_1}{x_1+x_2}\right) \frac{\beta^{\alpha_1+\alpha_2}}{\Gamma(\alpha_1) \Gamma(\alpha_2)} x_1^{\alpha_1-1} x_2^{\alpha_2 -1} e^{- \beta(x_1 + x_2)} dx_1 dx_2 \\ & = \int_0^1 \int_0^\infty h(z,u) \frac{\beta^{\alpha_1+\alpha_2}}{\Gamma(\alpha_1) \Gamma(\alpha_2)} (uz)^{\alpha_1-1} ((1-u)z)^{\alpha_2-1} e^{-\beta z} z du dz \\ &= \int_0^1 h(z,u) \frac{\Gamma(\alpha_1+\alpha_2)}{\Gamma(\alpha_1) \Gamma(\alpha_2)} u^{\alpha_1-1} (1-u)^{\alpha_2-1} du \int_0^\infty \frac{\beta^{\alpha_1+\alpha_2}}{\Gamma(\alpha_1+ \alpha_2)} z^{\alpha_1 + \alpha_2 -1} e^{-\beta z} dz \end{aligned} and this proves all three statements at once.

Remark This is a nice, indirect way, to compute the normalization for the density of the \beta distribution which is proportional to u^{\alpha_1-1}(1-u)^{\alpha_2-1}.

There is a natural connection between a binomial random variable (discrete) and the beta random variable (continuous) (let us call it P for a change). The pdf looks strangely similar {n \choose j} p^j (1-p)^{n-j} \quad \text{ versus } \frac{\Gamma(\alpha_1+\alpha_2)}{\Gamma(\alpha_1) \Gamma(\alpha_2)} p^{\alpha_1 -1} (1-p)^{\alpha_2 -1} But p is a parameter for the binomial while it is a variable for the second!

Model:

• Make n indepdent trials, each with a (random) probability P.

• Take P to have a beta distribution with suitably chosen parameter \alpha_1,\alpha_2.

• The mean \frac{\alpha_1}{\alpha_1+\alpha_2} is your average guess for the “true” probability p and by adjusting the scale you can adjust the variance (uncertainty) associated with your guess.

This leads to considering a random variable (X,P) taking value in \{0,1,\cdots,n\} \times [0,1] with a “density” f(j,p) = \underbrace{{n \choose j} p^j (1-p)^{n-j}}_{=k(p,j) \text{ kernel }} \underbrace{\frac{\Gamma(\alpha_1+\alpha_2)}{\Gamma(\alpha_1) \Gamma(\alpha_2)} p^{\alpha_1-1} (1-p)^{\alpha_2-1} }_{=f(p)} which is normalized \sum_{j=0}^n \int_0^1 f(k,p) dp =1

The beta-binomial distribution with paramters (n,\alpha_1,\alpha_2) is the marginal distribution of X on \{0,1,\cdots,n\} given by \begin{aligned} f(j) = \int_0^1 f(j,p) dp & = {n \choose j} \frac{\Gamma(\alpha_1+\alpha_2)}{\Gamma(\alpha_1) \Gamma(\alpha_2)} \int_0^1 p^{\alpha_1+n -1}(1-p)^{\alpha_2+n-k-1} \\ & = {n \choose j} \frac{\Gamma(\alpha_1+\alpha_2)}{\Gamma(\alpha_1) \Gamma(\alpha_2)} \frac{\Gamma(\alpha_1+j) \Gamma(\alpha_2+n-j)}{\Gamma(\alpha_1+\alpha_2+n)} \end{aligned}

Bayesian statistics framework

• We interpret the distribution f(p) with parameter \alpha_1 and \alpha_2 as the prior distribution which describe our beliefs before we do any experiment. For example \alpha_1=\alpha_2=1 correspond to a uniform distribution on p (that is we are totally agnostic, a fine choice if you know nothing).

• The beta-binomial which is the marginal f(j)=\int_0^1 f(j,p) dp describe the distribution of the independent trials under this model. It is called the evidence.

• The kernel k(p,j) is called the likelihood which describes the number of success, j, given a certain probability of success, p. It is called the likelihood when we view it as a function of p and think of j as a parameter.

• Now we can write the distribution using kernels in two ways: f(j,p)= k(p,j)f(p) = k(j,p) f(j) and the kernel k(j,p) is called the posterior distribution. It is interpreted as the distribution of p given that j trials have occured.

• We can rewrite this (this is just a version of Bayes theorem) as \text{posterior} = k(j,p) = \frac{k(p,j)f(p)}{f(j)} = \frac{\text{likelihood}\times\text{prior}}{\text{evidence}}\,.

• For the particular model at hand k(j,p) \propto p^{j} (1-p)^{n-j} p^{\alpha_1-1} (1-p)^{\alpha_2-1} \propto p^{\alpha_1+j-1} (1-p)^{\alpha_2+ n- j-1} and therefore k(j,p) has a binomial distribution with parameter \alpha_1 +j and \alpha_2+n-j.

• This is special: the prior and posterior distribution belong to the same family. We say that we have conjugate priors and this is a simple model to play. with. In general we need Monte-Carlo Markov chains to do the job.

• Example: Amazon seller ratings You want to buy a book online

Remarks

• We have not talked explicitly about integration of complex valued function h=f+ig where f and g are the real and imaginary part. It is simply defined as \int (f+ig)dP = \int f dP + i \int g dP provided f and g are integrable. A complex function h is integrable iff and only if |h| is intergable if and only if f and g are integrable. (The only thing a bit hard to prove is the triangle inequality |\int h dP|\le \int|h| dP.)

• The function e^{i\langle t,x \rangle} = \cos(\langle t,x \rangle) + i \sin (\langle t,x \rangle) is integrable since \sin(\langle t,x \rangle) and \cos(\langle t,x \rangle) are bounded function (thus in L^1 \subset L^\infty) or by noting that |e^{i\langle t,x \rangle}|=1.

• Suppose the measure P has a density f(x) then we have \widehat{P}(t)= \int e^{i\langle t,x \rangle} f(x) dx which is simply the Fourier transform (usually denoted by \widehat{f}(t)) of the function f(x). (Diverse conventions are used for the Fourier, e.g. using e^{-i 2\pi\langle k,x \rangle} instead e^{i\langle t,x \rangle})
  

As we have seen in ?@sec-inequalities the L^p spaces are form a dcereasing sequence L^1 \supset L^2 \supset L^3 \supset \cdots L^\infty and the next theorem show that if some random variable belongs to L^n (for some integer n) then its chararcteristic function will be n-times continuously differentiable.

The integrand in Equation 4.2 is bounded and converges as T \to \infty to the function \psi_{a,b}(x)= \left\{ \begin{array}{cl} 0 & x < a \\ \frac{1}{2} & x=a \\ 1 & a < x < b \\ \frac{1}{2} & x=b \\ 0 & x >b \\ \end{array} \right. By DCT we have that I_T \to \int \psi_{a,b} dP = P((a,b]) if a and b are not atoms. \quad \square

You can use the Fourier inversion formula to extract more information.

Another tool is the following convolution theorem

For the second part if X and Y have a density we have \begin{aligned} P(Z \in A) =E[1_A(Z)] & = \int \int 1_A(x+y) f_X(x) f_Y(y) dx dy \\ & = \int \int 1_A(z) f_X(z-y) f_Y(y) dz dy \quad \text{ change of variables } z=x+y, dz = dx \\ &= \int \left(\int f_X(z-y) f_Y(y) dy\right) 1_A(z) dz \quad \text{ Fubini } \end{aligned} and since this holds for all A, Z has the claimed density. The second formula is proved in the same way. \quad \square.

Example: triangular distribution
Suppose X and Y are independent and uniformly distributed on [-\frac{1}{2},\frac12]. Then Z=X+Y is between -1 and +1 for z \in [-1,1] we have f_Z(z)= \int_{-\infty}^\infty f_X(z-y) f_Y(y) dy = \left\{ \begin{array}{cl} \int_{-\frac12}^{z+ \frac12} dy & -1\le z \le 0 \\ \int_{z-\frac{1}{2}}^{\frac12} dy & 0 \le z \le 1 \end{array} = 1- |z| \right.

A stronger condition on the moments do imply uniqueness: if all moments exists and E[X^n] do not grow to fast with n then the moments do determine the distribution. This use a analytic continuation argument and relies on the uniqueness theorem for the Fourier transform.

• The next piece is the Taylor expansion theorem with reminder for function which are n-times continuously differentiable f(x)= \sum_{k=0}^n f^{(k)}(x_0) \frac{(x-x_0)^k}{k!} + \int_{x_0}^x \frac{f^{(n+1)}(t)}{n!}(x-s)^n dt from which we obtain
  \left|e^{itx}\left( e^{ihx} - \sum_{k=0}^n \frac{(ix)^k}{k!}\right)\right| \le \frac{|h x|^{n+1}}{(n+1)!}

• Integrating with respect to P and taking n \to \infty together with Theorem 4.2 gives \phi_X(t+h)= \sum_{k=0}^\infty \phi_X^{(k)}(t) \tag{4.3} for |h| < t_0.

• Now suppose X and Y have the same moment generating function in a neighborhood of 0. Then all their moments coincide and thus by Equation 4.3 (with t=0), \phi_X(t)=\phi_Y(t) on the interval (-r_0, r_0). By Theorem 4.2 their derivatives must also be equal on (-t_0,t_0). Using now Equation 4.3 (with t=-t_0+\epsilon and t=t_0-\epsilon shows that \phi_X(t)=\phi_Y(t) on the interval (-2t_0, 2t_0). Repeating this argument \phi_X(t)=\phi_Y(t) for all t and thus by Theorem 4.5 X and Y must have the same distribution. \quad \square