# PIG 14 - Uncertainty estimation¶

• Author: Christoph Deil, Axel Donath, Quentin Rémy, Fabio Acero

• Created: June 20, 2019

• Accepted: Nov 19, 2019

• Status: accepted

• Discussion: GH 2255

## Abstract¶

Currently Gammapy uses the uncertainties package to do error propagation for differential and integral flux of spectral models, e.g. to compute spectral model error bands. This is cumbersome since uncertainties doesn’t support astropy.units.Quantity (which we currently use in spectral model evaluation), and doesn’t work at all for some spectral models (e.g. EBL absorption and Naima), since uncertainties uses autodiff and needs explicit support for any Numpy, Scipy, Astropy, … function involved.

We propose to replace this with a custom uncertainty propagation implementation using finite differences. This will be less precise and slower than uncertainties, but it will work for any derived quantity and code that we call, and any spectral model.

We also propose to add support for a second method to propagate uncertainty from fit parameters to derived quantities, based on parameter samples. This is the standard technique in MCMC Bayesian analyses, but it can be used as well for normal likelihood analyses.

## Introduction¶

In Gammapy, gammapy.modeling and specifically the gammapy.modeling.Fit class support likelihood fitting to determine best-fit parameters, and to obtain a covariance matrix that represents parameter uncertainties and correlations. Parameter standard errors are obtained as the square root of the elements on the diagonal of the covariance matrix (see e.g. The interpretation of errors). Asymmetric errors and confidence intervals can be obtained via likelihood profile shape analysis, using methods on the gammapy.modeling.Fit class, partially re-implementing, partially calling the methods available in Minuit or Sherpa (see e.g. Fitting and Estimating Parameter Confidence Limits with Sherpa).

However, often one is interested not directly in a model parameter, but instead in a derived quantity that depends on multiple model parameters. Typical examples are that users want to compute errors or confidence intervals on quantities like differential or integral flux at or above certain energies, or location or extension of sources, from spectral and spatial model fits.

Here we will discuss two standard techniques to compute such uncertaintes:

1. Differentials (see Wikipedia - Propagation of uncertainty or uncertainties)

2. Monte carlo (MC) samples (see mcerp or astropy.uncertainty)

If you’re not familiar with the techniques, here’s a few references:

In Gammapy we currently use the uncertainties package to propagate errors to differential fluxes spectral_model.evaluate_error and integral fluxes spectral_model.integral_error. The evaluate_error method is called for an array of energies in plot_error, which is used to compute and plot spectral model error bands (see e.g. the SED fitting tutorial) It is using autodiff to compute differentials of derived quantities wrt. model parameters, which is accurate and fast where it works. However, uncertainties doesn’t support astropy.units.Quantity, making the current spectral model flux evaluation very convoluted, and it doesn’t work at all for some spectral models, namely non-analytical models like EBL absoption or Naima cosmic ray spectral models, which has been a frequent complaint by users (see GH 1046, GH 2007, GH 2190).

The MC sample method is most commonly used for Bayesian analysis, where the model fit directly results in a sample set that represents the parameter posterior probability distribution. A prototype example for that kind of analysis has been implemented in the MCMC Gammapy tutorial notebook. However, the MC sample error propagation technique can be applied in classical frequentist statistical analysis as well, if one considers it a numerical technique to avoid having to compute differentials, and instead samples a multivariate normal according to the best-fit parameters and covariance matrix, and then propagates the samples. One can scale the covariance matrix to only sample near the best-fit parameters and thus should be able to reproduce the differential method (within sampling noise).

## Proposal¶

We propose to change the SpectralModel.evaluate_error implementation to a custom differential based error propagation, as shown in the Gammapy uncertainty propagation prototype notebook. This allows us to completely remove the use of uncertainties within Gammapy, and to simplify the spectral model evaluation code. This will be a bit slower and less accurate than the current method, but it is a “black box” technique that will work for any spectral method or code that we call.

In the future we think that using an autodiff solution could be useful, not just for error propagation, but also for likelihood optimisation. It’s unlikely that we’d use uncertainties though, rather we’d probably use autograd or jax or even Tensorflow or PyTorch or Chainer or some other modern array computing package that supports autograd. So we think removing uncertainties now and cleaning up or model evaluation code is a good step, even if we want to change to some other framework later.

We also propose to add a method to generate parameter samples from the best-fit parameters and covariance, and to support MC error propagation. See Gammapy uncertainty propagation prototype notebook. This second part could be done before or after the v1.0 release, it’s independent of the first proposed change. The first step would be to add a test and improve the code of MC sampling interface (see GH 2304). Then probably we should add a Distribution object from astropy.uncertainty to Parameter or to a new FitMC class to store samples from MC analysis, or multinormal samples from the covariance matrix. And then possibly support for such objects in model evaluation and derived quantities. Another option could be to add support for “model sets” as in astropy.modeling to support arrays of parameter values within one model object, or to directly change gammapy.modeling to be based on astropy.modeling. As you can see, this second part of the proposal is a wish that Gammapy support MC sample based error propagation, the implementation is something to be prototyped and worked out in the future (could be now and for v1.0, or any time later).

## Alternatives¶

• Support only sample-based uncertainty propagation (like e.g. PyMC or 3ML)

• Support only differential uncertainty propagation (like e.g. Minuit)

• Keep everything as-is, use uncertainties

• Change to another autograd package like autograd or jax

## Decision¶

This proposal was discussed extensively on Github (GH 2255), and also in-person at the Gammapy coding sprint in Nov 2019. The exact mechanism and implementation for uncertainty propagation needs to be worked out (see the prototype notebook), this will happen at the coding sprint later this week. There were no objections to this proposal received, so it’s accepted.