Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data. In scientific fields ranging from population biology to cosmology, low-level mechanistic components are composed to create complex generative models. These models lead to intractable likelihoods and are typically non-differentiable, which poses challenges for traditional approaches to inference. We extend previous work in "inference compilation", which combines universal probabilistic programming and deep learning methods, to large-scale scientific simulators, and introduce a C++ based probabilistic programming library called CPProb. We successfully use CPProb to interface with SHERPA, a large code-base used in particle physics. Here we describe the technical innovations realized and planned for this library.Comment: 7 pages, 2 figure

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators—trained using model simulations—to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin–Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Statistical Learning and Inference at Particle Collider Experiments

Advances in data analysis techniques may play a decisive role in the discovery reach of particle collider experiments. However, the importing of expertise and methods from other data-centric disciplines such as machine learning and statistics faces significant hurdles, mainly due to the established use of different language and constructs. A large part of this document, also conceived as an introduction to the description of an analysis searching for non-resonant Higgs pair production in data collected by the CMS detector at the Large Hadron Collider (LHC), is therefore devoted to a broad redefinition of the relevant concepts for problems in experimental particle physics. The aim is to better connect these issues with those in other fields of research, so the solutions found can be repurposed. The formal exploration of the properties of the statistical models at particle colliders is useful to highlight the main challenges posed by statistical inference in this context: the multi-dimensional nature of the models, which can be studied only in a generative manner via forward simulation of observations, and the effect of nuisance parameters. The first issue can be tackled with likelihood-free inference methods coupled with the use of low-dimensional summary statistics, which may be constructed either with machine learning techniques or through physically motivated variables (e.g. event reconstruction). The second, i.e. the misspecification of the generative model which is addressed by the inclusion of nuisance parameters, reduces the effectiveness of summary statistics constructed with machine-learning techniques. A subset of the data analysis techniques formally discussed in the introductory part of the document are also exploited to study the non-resonant production process pp → HH → bbbb at the LHC in the context of the Standard Model (SM) and its extensions in effective fields theories (EFT), based on anomalous couplings of the Higgs field. Data collected in 2016 by the CMS detector and corresponding to a total of 35.9 fb−1 of proton-proton collisions are used to set an 95% confidence upper limit at 847 fb on the production cross section σ(pp → HH → bbbb) in the SM. Upper limits are also obtained for the cross sections corresponding to a representative set of points of the parameter space of EFT. The combination of those results with the ones obtained from the study of other decay channels of HH pairs is also discussed. In addition, the exercise of reformulating the goals of high energy physics analysis as a statistical inference problem is combined with modern machine learning technologies to develop a new technique, referred to as inference-aware neural optimisation. The technique produces summary statistics which directly minimise the expected uncertainty on the parameters of interest, optimally accounting for the effect of nuisance parameters. The application of this technique to a synthetic problem demonstrates that the obtained summary statistics are considerable more effective than those obtained with standard supervised learning methods, when the effect of the nuisance parameters is significant. Assuming its scalability to LHC data scenarios, this technique has ground-breaking potential for analyses dominated by systematic uncertainties