How to obtain moment bound from the importance sampling identity?

[WMDhamnekar]

TL;DR Summary

Let $m(t) =E[X^t]$ The moment bound states that for a > 0, $P\{ X \geq a \}\leq m(t)a^{-t} \forall t > 0$. How would you prove this result using importance sampling identity?

Let $X$ be a non-negative random variable and let a > 0. We want to bound the probability $P\{X \geq a\}$ in terms of the moments of X.
- Define a function $h(x) = \mathbb{1}\{x \geq a\}$, where $\mathbb{1}\{\cdot\}$ is the indicator function that returns 1 if the argument is true and 0 otherwise. Then, we have $P\{X \geq a\} = E_f[h(X)]$, where $E_f$ denotes the expected value with respect to the pdf of X.
- Choose another random variable Y with probability density function (pdf) $f_Y(y)$ such that $f_Y(y) > 0$ whenever $f_X(y) > 0$, where $f_X(x)$ is the pdf of X. This is called the importance distribution. Define the importance weight as $w(x) = f_X(x)/f_Y(x)$.
- Apply the importance sampling identity to write $E_f[h(X)] = E_g\left[\frac{h(Y)w(Y)}{g(Y)}\right]$ where the expectation on the right-hand side is taken with respect to Y.
Now how to proceed further? Can we use here Jensen's Inequality?

 

[WMDhamnekar]

My Answer:
The importance sampling identity states that for any measurable function f and random variable X with probability density function p, the expected value of f(X) can be expressed as:

$E[f(X)] = \int f(x) p(x) dx = \int f(x) \frac{p(x)}{q(x)} q(x) dx,$

where q is another probability density function that we choose. This identity allows us to estimate E[f(X)] by sampling from q instead of p.

Now, let's move on to proving the moment bound using the importance sampling identity. We want to show that for any positive number a and any positive exponent t, the probability that $X^t$ is greater than or equal to $a^t$ is bounded by $m(t) \cdot a^{-t}$.

To do this, we will choose the function $f(x) = \mathbb{I}(x \geq a)$, where $\mathbb{I}$ is the indicator function that takes the value 1 if the condition inside the parentheses is true, and 0 otherwise. By applying the importance sampling identity to $f(x^t)$, we have:

$E[f(X^t)] = \int \mathbb{I}(x^t \geq a^t) \frac{p(x^t)}{q(x^t)} q(x^t) dx^t.$

Now, notice that when $x^t \geq a^t$, the indicator function takes the value 1, and 0 otherwise. Therefore, we can rewrite the above expression as:

$E[f(X^t)] = \int \mathbb{I}(x^t \geq a^t) \frac{p(x^t)}{q(x^t)} q(x^t) dx^t = \int_{x^t \geq a^t} \frac{p(x^t)}{q(x^t)} q(x^t) dx^t.$

Now, let's focus on the integral $\int_{x^t \geq a^t} \frac{p(x^t)}{q(x^t)} q(x^t) dx^t$. We can rewrite this integral as the expectation of the random variable $\frac{p(X^t)}{q(X^t)} \cdot q(X^t)$, where $X^t$ is sampled from the distribution q. Therefore, we have:

$E[f(X^t)]=\int_{x^t \geq a^t} \frac{p(x^t)}{q(x^t)} q(x^t) dx^t = E\left[\frac{p(X^t)}{q(X^t)} \cdot q(X^t)\right].$

Now, since $\frac{p(X^t)}{q(X^t)} \cdot q(X^t)$ is a non-negative random variable, we can apply Markov's inequality, which states that for any non-negative random variable Y and any positive constant c, we have $P\{Y \geq c\} \leq \frac{E[Y]}{c}$. Applying Markov's inequality to our expression, we get:

$P\left\{\frac{p(X^t)}{q(X^t)} \cdot q(X^t) \geq {a^t}\right\} \leq \frac{E\left[\frac{p(X^t)}{q(X^t)} \cdot q(X^t)\right]}{a^t} =m(t) {a^{-t}}.$

But notice that the event $\left\{\frac{p(X^t)}{q(X^t)} \cdot q(X^t) \geq {a^t}\right\}$ is equivalent to the event $\left\{X^t \geq a^t\right\}$. Therefore, we have:

$P\left\{X^t \geq a^t\right\} \leq {m(t)}{a^{-t}}.$

And there you have it! We have successfully proved the moment bound using the importance sampling identity. This result tells us that for any positive number a and any positive exponent t, the probability that $X^t$ is greater than or equal to $a^t$ is bounded by $m(t) \cdot a^{-t}$.

I think now this answer seems to look correct. Isn't it?