TU Munich（慕尼黑工业大学）；University of Edinburgh（爱丁堡大学）；Center of Advanced European Studies Research (caesar)（欧洲高级研究中心）；Helmholtz-Zentrum Geesthacht（盖斯特哈赫特亥姆霍兹研究中心）；University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke
备注：
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann and Conor Durkan contributed equally in shared first authorship. This manuscript has been submitted for consideration to the Journal of Open Source Software (JOSS). 4 pages, no figures; v2: added link to sbi home
Scientists and engineers employ stochastic numerical simulators to model empirically observed phenomena. In contrast to purely statistical models, simulators express scientific principles that provide powerful inductive biases, improve generalization to new data or scenarios and allow for fewer, more interpretable and domain-relevant parameters. Despite these advantages, tuning a simulator's parameters so that its outputs match data is challenging. Simulation-based inference (SBI) seeks to identify parameter sets that a) are compatible with prior knowledge and b) match empirical observations. Importantly, SBI does not seek to recover a single 'best' data-compatible parameter set, but rather to identify all high probability regions of parameter space that explain observed data, and thereby to quantify parameter uncertainty. In Bayesian terminology, SBI aims to retrieve the posterior distribution over the parameters of interest. In contrast to conventional Bayesian inference, SBI is also applicable when one can run model simulations, but no formula or algorithm exists for evaluating the probability of data given parameters, i.e. the likelihood. We present $\texttt{sbi}$, a PyTorch-based package that implements SBI algorithms based on neural networks. $\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
Aided by advances in neural density estimation, considerable progress has been made in recent years towards a suite of simulation-based inference (SBI) methods capable of performing flexible, black-box, approximate Bayesian inference for stochastic simulation models. While it has been demonstrated that neural SBI methods can provide accurate posterior approximations, the simulation studies establishing these results have considered only well-specified problems -- that is, where the model and the data generating process coincide exactly. However, the behaviour of such algorithms in the case of …
University of Tübingen（图宾根大学）；TU Munich（慕尼黑工业大学）；Helmholtz Centre Hereon（亥姆霍兹Hereon中心）
Simulation-based inference (SBI) refers to statistical inference on stochastic models for which we can generate samples, but not compute likelihoods. Like SBI algorithms, generative adversarial networks (GANs) do not require explicit likelihoods. We study the relationship between SBI and GANs, and introduce GATSBI, an adversarial approach to SBI. GATSBI reformulates the variational objective in an adversarial setting to learn implicit posterior distributions. Inference with GATSBI is amortised across observations, works in high-dimensional posterior spaces and supports implicit priors. We eval…
University of Southampton（南安普顿大学）；Imperial College, London（伦敦帝国理工学院）
A key challenge in crowdsourcing is inferring the ground truth from noisy and unreliable data. To do so, existing approaches rely on collecting redundant information from the crowd, and aggregating it with some probabilistic method. However, oftentimes such methods are computationally inefficient, are restricted to some specific settings, or lack theoretical guarantees. In this paper, we revisit the problem of binary classification from crowdsourced data. Specifically we propose Streaming Bayesian Inference for Crowdsourcing (SBIC), a new algorithm that does not suffer from any of these limita…
Spectra: Exact Component Transport for Test-Time Prior Adaptation in Simulation-Based Inference
Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；International Max Planck Research School on Cognitive NeuroImaging（马克斯·普朗克国际认知神经影像研究学院）；Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据分析与人工智能中心（ScaDS.AI））；Leipzig University（莱比锡大学）；University College London（伦敦大学学院）
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings
Chalmers and University of Gothenburg（查尔姆斯理工大学与哥德堡大学）；Univ. Grenoble Alpes（格勒诺布尔大学）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）；Inserm U1216（法国国家健康与医学研究院U1216研究所）；CHU Grenoble Alpes（格勒诺布尔阿尔卑斯大学医院中心）；Grenoble Institut des Neurosciences（格勒诺布尔神经科学研究所）；LJK（让·昆塔纳计算实验室）；Univ. Lyon（里昂大学）；INSA Lyon（里昂国立应用科学学院）；UCBL（里昂第一大学）；CREATIS（图像与信息系统在成像与治疗中的应用研究实验室）；Dept. Mathematical Sciences（数学科学系）
Machine Learning in Science, University of Tübingen（机器学习科学系，图宾根大学）；Tübingen AI Center（图宾根人工智能中心）；TransferLab, appliedAI Institute for Europe（TransferLab，应用AI欧洲研究所）；Helmholtz AI and Helmholtz-Zentrum Dresden-Rossendorf（海德堡人工智能与德累斯顿-罗斯托克研究中心）；Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France（巴黎-萨克雷大学，法国国家信息与自动化研究所，法国原子能委员会，帕莱伊索，91120）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学，法国国家信息与自动化研究所，国家科学研究中心，格勒诺布尔INP，LJK）；University of Amsterdam（阿姆斯特丹大学）；VIB-Neuroelectronics Research Flanders (NERF), Belgium（比利时弗拉芒神经电子研究VIB-NERF）；Departments of Computer Science and Electrical Engineering, KU Leuven, Belgium（比利时鲁文大学计算机科学与电气工程系）；Google Research（谷歌研究）；Department Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany（经验推理部门，马克斯·普朗克智能系统研究所，图宾根，德国）
Preconditioned Neural Posterior Estimation for Likelihood-free Inference
School of Mathematical Sciences, Queensland University of Technology（昆士兰科技大学数学科学学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）
CP4SBI: Local Conformal Calibration of Credible Sets in Simulation-Based Inference
Department of Statistics, Federal University of São Carlos（统计系，圣卡洛斯联邦大学）；Institute of Mathematics and Computer Science, University of São Paulo（数学与计算机科学学院，圣保罗大学）；Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学，法国国家信息与自动化研究所，法国国家科学研究中心，格勒诺布尔INP，LJK）
Simulation-based Bayesian inference under model misspecification
School of Mathematical Sciences, Queensland University of Technology（昆士兰科技大学数学科学学院）；Centre for Data Science, Queensland University of Technology（昆士兰科技大学数据科学中心）；Department of Econometrics and Business Statistics, Monash University（莫纳什大学计量经济学与商业统计系）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；Institute for Adaptive & Neural Computation, University of Edinburgh（爱丁堡大学自适应与神经计算研究所）；Centre of Excellence for the Mathematical Analysis of Cellular Systems, Queensland University of Technology（昆士兰科技大学细胞系统数学分析卓越中心）
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Tübingen AI Center（图宾根人工智能中心）；Machine Learning in Science, Excellence Cluster Machine Learning, University of Tübingen（图宾根大学机器学习卓越集群科学机器学习系）；Department Empirical Inference, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所经验推断系）
Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions. The development of appropriate validation methods however has fallen behind. Indeed, most of the existing metrics either require access to the true posterior distribution, or fail to provide theoretical guarantees on the consistency of the inferred approximation beyond the one-dimensional setting. This work proposes easy to interpret validation diagnostics for multi-dime…
German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science（认知科学中心）；Technical University of Darmstadt（达姆施塔特工业大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）
Simulation-based inference via telescoping ratio estimation for trawl processes
Statistics Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST)（国王 Abdullah 科学与技术大学）；Department of Mathematics, Imperial College London（伦敦帝国理工学院数学系）
Misspecification-robust amortised simulation-based inference using variational methods
Institute of Astronomy, University of Cambridge（天文研究所，剑桥大学）；Statistical Laboratory, University of Cambridge（统计实验室，剑桥大学）；Kavli Institute for Cosmology, University of Cambridge（宇宙学研究所，剑桥大学）；Institute of Astrophysics, FORTH（天体物理学研究所，福蒂斯研究所）
This paper presents recent methodological advances to perform simulation-based inference (SBI) of a general class of Bayesian hierarchical models (BHMs), while checking for model misspecification. Our approach is based on a two-step framework. First, the latent function that appears as second layer of the BHM is inferred and used to diagnose possible model misspecification. Second, target parameters of the trusted model are inferred via SBI. Simulations used in the first step are recycled for score compression, which is necessary to the second step. As a proof of concept, we apply our framewor…
Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method …
FlowSN: Neural Simulation-Based Inference under Realistic Selection Effects applied to Supernova Cosmology
University of Cambridge（剑桥大学）；National Center for Supercomputing Applications（国家超级计算应用中心）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Chicago（芝加哥大学）
Simulation-based Inference with the Python Package sbijax
Swiss Data Science Center and ETH Zurich, Switzerland（瑞士数据科学中心和苏黎世联邦理工学院，瑞士）；Università della Svizzera italiana, Switzerland, and Insubria University, Italy（瑞士意大利大学，瑞士，和意大利Insubria大学）；Swiss Federal Institute of Aquatic Science and Technology, Switzerland（瑞士联邦水产科学与技术研究所，瑞士）
College of Information and Computer Sciences, University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校信息与计算机科学学院）；Basis Research Institute（贝西斯研究院）；Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology（麻省理工学院脑与认知科学系）
A growing family of approaches to causal inference rely on Bayesian formulations of assumptions that go beyond causal graph structure. For example, Bayesian approaches have been developed for analyzing instrumental variable designs, regression discontinuity designs, and within-subjects designs. This paper introduces simulation-based identifiability (SBI), a procedure for testing the identifiability of queries in Bayesian causal inference approaches that are implemented as probabilistic programs. SBI complements analytical approaches to identifiability, leveraging a particle-based optimization …
Dynamic SBI: Round-free Sequential Simulation-Based Inference with Adaptive Datasets
GRAPPA Institute, Institute for Theoretical Physics Amsterdam, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands（GRAPPA研究所、阿姆斯特丹理论物理研究所、阿姆斯特丹大学、科学公园904号）；Kavli Institute for Cosmology Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom（剑桥卡文迪许研究所）；Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, United Kingdom（天文学研究所）
Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
Kavli IPMU (WPI), UTIAS, The University of Tokyo, Japan（Kavli IPMU（WPI）、UTIAS、东京大学）；Center for Data-Driven Discovery, Kavli IPMU (WPI), Japan（数据驱动发现中心、Kavli IPMU（WPI）、日本）；Center for Computational Astrophysics, Flatiron Institute, New York, USA（计算天体物理学中心、Flatiron研究所、美国纽约）；Department of Astrophysical Sciences, Princeton University, USA（天体物理学系、普林斯顿大学、美国）；RCAST, The University of Tokyo, Japan（RCAST、东京大学、日本）
Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
Machine Learning in Science, University of Tübingen（图宾根大学机器学习与科学系）；Tübingen AI Center（图宾根人工智能中心）；Department Empirical Inference, Max Planck Institute for Intelligent Systems（智能系统研究所经验推断部门）
University of Tübingen（蒂宾根大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit likelihood evaluations. A prime example for the application of SBI in neuroscience involves estimating the parameters governing the response dynamics of Hodgkin-Huxley (HH) models from electrophysiological measurements, by inferring a posterior over the parameters that is consistent with a set of observations. To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to es…
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys. Despite continual improvements to the quality of density estimation by learned models, applications of such techniques to real data are entirely reliant on the generalization power of neural networks far outside the training distribution, which is mostly unconstrained. Due to the imperfections in scientist-created simulations, and the large computational expense of generating all possible parameter combinations, SBI methods in cosmology are vulner…
The Pennsylvania State University（宾夕法尼亚州立大学）；University of Toronto（多伦多大学）
Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution. However, the huge amount of data also poses an immediate computational challenge: current tools for inferring parameters from the light of galaxies take $\gtrsim 10$ hours per fit. This is prohibitively expensive. Simulation-based Inference (SBI) is a promising solution. However, it requires simulated data with identical characteristics to the observed data, whereas real astronomical surveys are often hi…
