In this white paper we introduce the IMAGINE Consortium and its scientific background, goals and structure. The purpose of the consortium is to coordinate and facilitate the efforts of a diverse group of researchers in the broad areas of the interstellar medium, Galactic magnetic fields and cosmic rays, and our overarching goal is to develop more comprehensive insights into the structures and roles of interstellar magnetic fields and their interactions with cosmic rays within the context of Galactic astrophysics. The ongoing rapid development of observational and numerical facilities and techniques has resulted in a widely felt need to advance this subject to a qualitatively higher level of self-consistency, depth and rigour. This can only be achieved by the coordinated efforts of experts in diverse areas of astrophysics involved in observational, theoretical and numerical work. We present our view of the present status of this research area, identify its key unsolved problems and suggest a strategy that will underpin our work. The backbone of the consortium is the Interstellar MAGnetic field INference Engine, a publicly available Bayesian platform that employs robust statistical methods to explore the multi-dimensional likelihood space using any number of modular inputs. This tool will be used by the IMAGINE Consortium to develop an interpretation and modelling framework that provides the method, power and flexibility to interfuse information from a variety of observational, theoretical and numerical lines of evidence into a self-consistent and comprehensive picture of the thermal and non-thermal interstellar media. An important innovation is that a consistent understanding of the phenomena that are directly or indirectly influenced by the Galactic magnetic field, such as the deflection of ultra-high energy cosmic rays or extragalactic backgrounds, is made an integral part of the modelling. The IMAGINE Consortium, which is informal by nature and open to new participants, hereby presents a methodological framework for the modelling and understanding of Galactic magnetic fields that is available to all communities whose research relies on a state of the art solution to this problem.

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We have built Prism, a Probabilistic Regression Instrument for Simulating Models. Prism uses the Bayes linear approach and history matching to construct an approximation ('emulator') of any given model, by combining limited model evaluations with advanced regression techniques, covariances and probability calculations. It is designed to easily facilitate and enhance existing Markov chain Monte Carlo (MCMC) methods by restricting plausible regions and exploring parameter space efficiently. However, Prism can additionally be used as a standalone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. With Prism, the time spent on evaluating a model is minimized, providing developers with an advanced model analysis for a fraction of the time required by more traditional methods.This paper provides an overview of the different techniques and algorithms that are used within Prism. We demonstrate the advantage of using the Bayes linear approach over a full Bayesian analysis when analyzing complex models. Our results show how much information can be captured by Prism and how one can combine it with MCMC methods to significantly speed up calibration processes (>15 times faster). Prism is an open-source Python package that is available under the BSD 3-Clause License (BSD-3) at https://github.com/1313e/PRISM and hosted at https://prism-tool.readthedocs.io. Prism has also been reviewed by The Journal of Open Source Software (van der Velden 2019).