Transforms of random variates

June 4, 2020 — May 14, 2021

Figure 1

I have a nonlinear transformation of a random process. What is its distribution?

Related: What is the gradient of the transform? That is the topic of the reparameterization trick and other MC grad estimators. This is very commonly seen in the context of transforms of Gaussians when it produces results like the delta method and extended Kalman filtering.

1 Taylor expansion

Not complicated but subtle (Gustafsson and Hendeby 2012).

Consider a general nonlinear differentiable transformation \(g\) and its second order Taylor expansion. Consider the mapping \(g:\mathbb{R}^{n_{x}}\to\mathbb{R}^{n_{z}}\) applied to a variable \(x,\) defining \(z:=g(x).\) Let \(\mathrm{E}(x)=\mu_{x}\) and \(\operatorname{Var}(x)=P_{x}.\) The Hessian of the \(i^{\text {th }}\) component of \(g\) is denoted \(g_{i}^{\prime \prime}.\) \([x_i]_i\) is a vector where the \(i\)th element is \(x_i\). We will approximate \(z\) using the Taylor expansion, \[z=g\left(\mu_{x}\right)+g^{\prime}\left(\mu_{x}\right)\left(x-\mu_{x}\right)+\left[\frac{1}{2}\left(x-\mu_{x}\right)^{T} g_{i}^{\prime \prime}\left(\mu_{x}\right)\left(x-\mu_{x}\right)\right]_{i}.\] Leaving aside questions of when this is convergent for now. Then the first moment of \(z\) is given by \[ \mu_{z}=g\left(\mu_{x}\right)+\frac{1}{2}\left[\operatorname{tr}\left(g_{i}^{\prime \prime}\left(\mu_{x}\right) P_{x}\right)\right]_{i} \] Further, let \(x \sim \mathcal{N}\left(\mu_{x}, P_{x}\right)\), then the second moment of \(z\) is given by \[ P_{z}=g^{\prime}\left(\mu_{x}\right) P_{x}\left(g^{\prime}\left(\mu_{x}\right)\right)^{T}+\frac{1}{2}\left[\operatorname{tr}\left(g_{i}^{\prime \prime}\left(\mu_{x}\right) P_{x} g_{j}^{\prime \prime}\left(\mu_{x}\right) P_{x}\right)\right]_{i j} \] with \(i, j=1, \ldots, n_{z}.\)

This is commonly seen in the context of transforms of Gaussians.

2 Unscented transforms

Typically seen for Gaussian RVs. Ebeigbe et al. (2021) claims to have devised a method for more general RVs.

3 Stein’s lemma

As seen in Stein’s method. Gives us the special case of certain exponential RVS (typically Gaussian) under certain matched transforms. Long story.

4 Stochastic Itô-Taylor expansion

Taylor expansions for stochastic processes. See stochastic taylor expansion. tl;dr: Usually more trouble than it is worth.

5 Learnable transforms

See reparameterization trick.

6 References

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Ariffin, and Rosli. 2017. Stochastic Taylor Expansion of Derivative-Free Method for Stochastic Differential Equations.” Malaysian Journal of Fundamental and Applied Sciences.
Collard, and Juillard. 2001. Accuracy of Stochastic Perturbation Methods: The Case of Asset Pricing Models.” Journal of Economic Dynamics and Control.
Easley, and Berry. 2020. A Higher Order Unscented Transform.” arXiv:2006.13429 [Cs, Math].
Ebeigbe, Berry, Norton, et al. 2021. A Generalized Unscented Transformation for Probability Distributions.” ArXiv.
Gustafsson, and Hendeby. 2008. On Nonlinear Transformations of Stochastic Variables and Its Application to Nonlinear Filtering.” In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.
———. 2012. Some Relations Between Extended and Unscented Kalman Filters.” IEEE Transactions on Signal Processing.
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Jin, Judd, and Insitution. n.d. “Perturbation Methods for General Dynamic Stochastic Models.”
Kloeden, P. E., and Platen. 1991. Stratonovich and Ito Stochastic Taylor Expansions.” Mathematische Nachrichten.
Kloeden, Peter E., and Platen. 1992. Stochastic Taylor Expansions.” In Numerical Solution of Stochastic Differential Equations. Applications of Mathematics.
———. 2010. Numerical Solution of Stochastic Differential Equations.
Kloeden, P. E., Platen, and Wright. 1992. The Approximation of Multiple Stochastic Integrals.” Stochastic Analysis and Applications.
Lin, Khan, and Schmidt. 2019. Stein’s Lemma for the Reparameterization Trick with Exponential Family Mixtures.” arXiv:1910.13398 [Cs, Stat].
Majumdar, and Majumdar. 2019. On the Conditional Distribution of a Multivariate Normal Given a Transformation – the Linear Case.” Heliyon.
Papapantoleon, and Siopacha. 2010. Strong Taylor Approximation of Stochastic Differential Equations and Application to the Lévy LIBOR Model.” arXiv:0906.5581 [Math, q-Fin].
Rößler. 2004. Stochastic Taylor Expansions for the Expectation of Functionals of Diffusion Processes.” Stochastic Analysis and Applications.
Roth, Hendeby, and Gustafsson. 2016. Nonlinear Kalman Filters Explained: A Tutorial on Moment Computations and Sigma Point Methods.” Journal of Advances in Information Fusion.
Schmitt-Grohe, and Uribe. n.d. “Perturbation Methods for the Numerical Analysis of DSGE Models: Lecture Notes.”
Schmitt-Grohé, and Uribe. 2004. Solving Dynamic General Equilibrium Models Using a Second-Order Approximation to the Policy Function.” Journal of Economic Dynamics and Control.
Schoutens, Leuven, and Studer. 2001. Stochastic Taylor Expansions for Poisson Processes and Applications Towards Risk Management.”
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