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Sub-Gaussian property for the Beta distribution (part 3, final)

Posted in General, R by Julyan Arbel on 26 December 2017
beta_to_bernoulli

When a Beta random variable wants to act like a Bernoulli: convergence of optimal proxy variance.

In this third and last post about the Sub-Gaussian property for the Beta distribution [1] (post 1 and post 2), I would like to show the interplay with the Bernoulli distribution as well as some connexions with optimal transport (OT is a hot topic in general, and also on this blog with Pierre’s posts on Wasserstein ABC). (more…)

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Sub-Gaussian property for the Beta distribution (part 2)

Posted in R by Julyan Arbel on 20 December 2017

 

Left: What makes the Beta optimal proxy variance (red) so special? Right: The difference function has a double zero (black dot).

As a follow-up on my previous post on the sub-Gaussian property for the Beta distribution [1], I’ll give here a visual illustration of the proof.

A random variable X with finite mean \mu=\mathbb{E}[X] is sub-Gaussian if there is a positive number \sigma such that:

\mathbb{E}[\exp(\lambda (X-\mu))]\le\exp\left(\frac{\lambda^2\sigma^2}{2}\right)\,\,\text{for all } \lambda\in\mathbb{R}.

We focus on X being a Beta(\alpha,\beta) random variable. Its moment generating function \mathbb{E}[\exp(\lambda X)] is known as the Kummer function, or confluent hypergeometric function _1F_1(\alpha,\alpha+\beta,\lambda). So is \sigma^2-sub-Gaussian as soon as the difference function

u_\sigma(\lambda)=\exp\left(\frac{\alpha}{\alpha+\beta}\lambda+\frac{\sigma^2}{2}\lambda^2\right)-_1F_1(\alpha,\alpha+\beta,\lambda)

remains positive on \mathbb{R}. This difference function u_\sigma(\cdot) is plotted on the right panel above for parameters (\alpha,\beta)=(1,1.3). In the plot, \sigma^2 is varying from green for the variance \text{Var}[X]=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)} (which is a lower bound to the optimal proxy variance) to blue for the value \frac{1}{4(\alpha+\beta+1)}, a simple upper bound given by Elder (2016), [2]. The idea of the proof is simple: the optimal proxy-variance corresponds to the value of \sigma^2 for which u_\sigma(\cdot) admits a double zero, as illustrated with the red curve (black dot). The left panel shows the curves with \mu = \frac{\alpha}{\alpha+\beta} varying, interpolating from green for \text{Var}[X]=\frac{\alpha\beta}{(\alpha+\beta)^2(\alpha+\beta+1)} to blue for \frac{1}{4(\alpha+\beta+1)}, with only one curve qualifying as the optimal proxy variance in red.

References

[1] Marchal and Arbel (2017), On the sub-Gaussianity of the Beta and Dirichlet distributions. Electronic Communications in Probability, 22:1–14, 2017. Code on GitHub.
[2] Elder (2016), Bayesian Adaptive Data Analysis Guarantees from Subgaussianity, https://arxiv.org/abs/1611.00065

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Sub-Gaussian property for the Beta distribution (part 1)

Posted in General by Julyan Arbel on 2 May 2017

 

With my friend Olivier Marchal (mathematician, not filmmaker, nor the cop), we have just arXived a note on the sub-Gaussianity of the Beta and Dirichlet distributions.

The notion, introduced by Jean-Pierre Kahane, is as follows:

A random variable X with finite mean \mu=\mathbb{E}[X] is sub-Gaussian if there is a positive number \sigma such that:

\mathbb{E}[\exp(\lambda (X-\mu))]\le\exp\left(\frac{\lambda^2\sigma^2}{2}\right)\,\,\text{for all } \lambda\in\mathbb{R}.

Such a constant \sigma^2 is called a proxy variance, and we say that X is \sigma^2-sub-Gaussian. If X is sub-Gaussian, one is usually interested in the optimal proxy variance:

 \sigma_{\text{opt}}^2(X)=\min\{\sigma^2\geq 0\text{ such that } X \text{ is } \sigma^2\text{-sub-Gaussian}\}.

Note that the variance always gives a lower bound on the optimal proxy variance: \text{Var}[X]\leq \sigma_{\text{opt}}^2(X). In particular, when \sigma_{\text{opt}}^2(X)=\text{Var}[X], X is said to be strictly sub-Gaussian.

The sub-Gaussian property is closely related to the tails of the distribution. Intuitively, being sub-Gaussian amounts to having tails lighter than a Gaussian. This is actually a characterization of the property. Let Z\sim\mathcal{N}(0,1). Then:

X \text{ is sub-Gaussian } \iff \exists c, \forall x\geq0:\, \mathsf{P}(|X-\mathbb{E}[X]|\geq x) \leq c\mathsf{P}(|Z|\geq x).

That equivalence clearly implies exponential upper bounds for the tails of the distribution since a Gaussian Z\sim\mathcal{N}(0,\sigma^2) satisfies

\mathsf{P}(Z\ge x)\le\exp(-\frac{x^2}{2\sigma^2}).

That can also be seen directly: for a \sigma^2-sub-Gaussian variable X,

\forall\, \lambda>0\,:\,\,\mathsf{P}(X-\mu\geq x) = \mathsf{P}(e^{\lambda(X-\mu)}\geq e^{\lambda x})\leq \frac{\mathbb{E}[e^{\lambda(X-\mu)}]}{e^{\lambda x}}\quad\text{by Markov inequality,}

\leq\exp(\frac{\sigma^2\lambda^2}{2}-\lambda x)\quad\text{by sub-Gaussianity.}

The polynomial function \lambda\mapsto \frac{\sigma^2\lambda^2}{2}-\lambda x is minimized on \mathbb{R}_+ at \lambda = \frac{x}{\sigma^2}, for which we obtain

\mathsf{P}(X-\mu\geq x) \leq\exp(-\frac{x^2}{2\sigma^2}).

In that sense, the sub-Gaussian property of any compactly supported random variable X comes for free since in that case the tails are obviously lighter than those of a Gaussian. A simple general proxy variance is given by Hoeffding’s lemma. Let X be supported on [a,b] with \mathbb{E}[X]=0. Then for any \lambda\in\mathbb{R},

\mathbb{E}[\exp(\lambda X)]\leq\exp\left(\frac{(b-a)^2}{8}\lambda^2\right)

so X is \frac{(b-a)^2}{4}-sub-Gaussian.

Back to the Beta where [a,b]=[0,1], this shows the Beta is \frac{1}{4}-sub-Gaussian. The question of finding the optimal proxy variance is a more challenging issue. In addition to characterizing the optimal proxy variance of the Beta distribution in the note, we provide the simple upper bound \frac{1}{4(\alpha+\beta+1)}. It matches with Hoeffding’s bound for the extremal case \alpha\to0, \beta\to0, where the Beta random variable concentrates on the two-point set \{0,1\} (and when Hoeffding’s bound is tight).

In getting the bound \frac{1}{4(\alpha+\beta+1)}, we prove a recent conjecture made by Sam Elder in the context of Bayesian adaptive data analysis. I’ll say more about getting the optimal proxy variance in a next post soon.

Cheers!

Julyan

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