ghq 0.0.5

Gauss-Hermite quadrature in JAX

ghq

Gauss-Hermite quadrature in JAX

Installed easily from PyPI:

pip install ghq

Univariate Gaussian integrals

Gauss-Hermite quadrature is a method for numerically integrating functions of the form:

$$ \int_{-\infty}^{\infty} f(x) \mathbf{N}(x \mid \mu, \sigma^2) dx \approx \sum_{i=1}^\mathsf{degree} w_i f(x_i), $$

where $\lbrace x_i, w_i \rbrace_{i=1}^\mathsf{degree}$ are chosen deterministically. The integral approximation can be executed easily with ghq:

ghq.univariate(f, mu, sigma, degree=32)

where f: Callable[[float], Array] is a JAX vectorisable function and degree is an optional argument controlling the number of function evalutions, increasing degree increases the accuracy of the integral. (Note that the output of f can be multivariate but the input is univariate).

Univariate unbounded integrals

More generally, we can use an importance-sampling-like approach to integrate functions of the form:

$$ \int_{-\infty}^{\infty} f(x) dx = \int_{-\infty}^{\infty} \frac{f(x)}{\mathbf{N}(x \mid \mu, \sigma^2)} \mathbf{N}(x \mid \mu, \sigma^2) dx \approx \sum_{i=1}^\mathsf{degree} w_i \frac{f(x_i)}{\mathbf{N}(x_i \mid \mu, \sigma^2)}, $$

and with ghq:

ghq.univariate_importance(f, mu, sigma, degree=32)

Univariate half-bounded integrals

Consider lower-bounded integrals of the form:

$$ \int_{a}^{\infty} f(x) dx =\int_{-\infty}^{\infty} f(a + e^y) e^y dy \approx \sum_{i=1}^\mathsf{degree} w_i \frac{f(a + e^{y_i}) e^{y_i}}{\mathbf{N}(y_i \mid \mu, \sigma^2)}, $$

where we use the transformation $y = \log(x - a)$ to map the lower-bounded integral to an unbounded integral. This can be approximated with ghq:

ghq.univariate_importance(f, mu, sigma, degree=32, lower=a)

or for upper-bounded integrals over $[-\infty, b)$ using transformation $y = \log(b - x)$:

ghq.univariate_importance(f, mu, sigma, degree=32, upper=b)

Univariate bounded integrals

For doubly-bounded integrals in $[a, b)$ we have

$$ \int_{a}^{b} f(x) dx = \int_{-\infty}^{\infty} f\left(a + (b-a)\text{logit}^{-1}(y)\right) (b-a) \text{logit}^{-1}(y) \left(1-\text{logit}^{-1}(y)\right) dy, $$

where we use the transfomation $y=\text{logit}(\frac{x-a}{b-a})$ with $\text{logit}(u)=\log\frac{u}{1-u}$ and $\text{logit}^{-1}(v) = \frac{1}{1+e^{-v}}$.

In ghq we have:

ghq.univariate_importance(f, mu, sigma, degree=32, lower=a, upper=b).

The Stan reference manual provides an excellent reference for transformations of variables.

Multivariate Gaussian integrals

$$ \int f(x) \mathbf{N}(x \mid \mu, \Sigma) dx, $$

in ghq is:

ghq.multivariate(f, mu, Sigma, degree=32)

where f: Callable[[Array], Array] is a function that takes a multivariate input. Beware though that multivariate Gauss-Hermite quadrature has complexity $O(\text{degree}^d)$ where $d$ is the dimension of the integral, so it is not feasible for high-dimensional integrals.

Multivariate unbounded integrals

For generic multivariate integrals

$$ \int f(x) dx = \int \frac{f(x)}{\mathbf{N}(x \mid \mu, \Sigma)} \mathbf{N}(x \mid \mu, \Sigma) dx, $$

in ghq is:

ghq.multivariate_importance(f, mu, Sigma, degree=32)

Although as above this is not feasible for high-dimensional integrals due to $O(\text{degree}^d)$ cost. Additionally only expect good performance when the domain of $f$ is approximately the same as the domain of high probability mass for the Gaussian distribution.

Multivariate bounded integrals

Coming soon...

Citation

If you use ghq in your research, please cite it using the following BibTeX entry:

@software{ghq,
  author = {Duffield, Samuel},
  title = {ghq: Gauss-Hermite quadrature in JAX},
  year = {2024},
  url = {https://github.com/SamDuffield/ghq}
}