Zeitschriftenartikel: „Simulation-Based Inference“

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Gourieroux, Christian, und Alain Monfort. „Simulation-based inference“. Journal of Econometrics 59, Nr. 1-2 (September 1993): 5–33. http://dx.doi.org/10.1016/0304-4076(93)90037-6.

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Vogelsang, T. J. „Simulation-Based Inference in Econometrics“. Journal of the American Statistical Association 97, Nr. 458 (Juni 2002): 657. http://dx.doi.org/10.1198/jasa.2002.s478.

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3

Brehmer, Johann. „Simulation-based inference in particle physics“. Nature Reviews Physics 3, Nr. 5 (22.03.2021): 305. http://dx.doi.org/10.1038/s42254-021-00305-6.

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4

Sheu, Ching-fan, und Suzanne L. O’Curry. „Simulation-based bayesian inference using BUGS“. Behavior Research Methods, Instruments, & Computers 30, Nr. 2 (Juni 1998): 232–37. http://dx.doi.org/10.3758/bf03200649.

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Cranmer, Kyle, Johann Brehmer und Gilles Louppe. „The frontier of simulation-based inference“. Proceedings of the National Academy of Sciences 117, Nr. 48 (29.05.2020): 30055–62. http://dx.doi.org/10.1073/pnas.1912789117.

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Many domains of science have developed complex simulations to describe phenomena of interest. While these simulations provide high-fidelity models, they are poorly suited for inference and lead to challenging inverse problems. We review the rapidly developing field of simulation-based inference and identify the forces giving additional momentum to the field. Finally, we describe how the frontier is expanding so that a broad audience can appreciate the profound influence these developments may have on science.

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Gouriéroux und Monfort. „Simulation Based Inference in Models with Heterogeneity“. Annales d'Économie et de Statistique, Nr. 20/21 (1990): 69. http://dx.doi.org/10.2307/20075807.

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7

Ghysels, Khalaf und Vodounou. „Simulation Based Inference in Moving Average Models“. Annales d'Économie et de Statistique, Nr. 69 (2003): 85. http://dx.doi.org/10.2307/20076364.

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Jasra, Ajay, David A. Stephens und Christopher C. Holmes. „On population-based simulation for static inference“. Statistics and Computing 17, Nr. 3 (27.07.2007): 263–79. http://dx.doi.org/10.1007/s11222-007-9028-9.

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9

McKinley, Trevelyan J., Joshua V. Ross, Rob Deardon und Alex R. Cook. „Simulation-based Bayesian inference for epidemic models“. Computational Statistics & Data Analysis 71 (März 2014): 434–47. http://dx.doi.org/10.1016/j.csda.2012.12.012.

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Tejero-Cantero, Alvaro, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro Gonçalves, David Greenberg und Jakob Macke. „sbi: A toolkit for simulation-based inference“. Journal of Open Source Software 5, Nr. 52 (21.08.2020): 2505. http://dx.doi.org/10.21105/joss.02505.

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Manrique, Aurora, und Neil Shephard. „Simulation‐based likelihood inference for limited dependent processes“. Econometrics Journal 1, Nr. 1 (01.06.1998): C174—C202. http://dx.doi.org/10.1111/1368-423x.11010.

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12

Shapiro, Alexander. „Simulation-based optimization—convergence analysis and statistical inference“. Communications in Statistics. Stochastic Models 12, Nr. 3 (Januar 1996): 425–54. http://dx.doi.org/10.1080/15326349608807393.

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13

Edwards, Don. „Exact simulation-based inference: A survey, with additions“. Journal of Statistical Computation and Simulation 22, Nr. 3-4 (September 1985): 307–26. http://dx.doi.org/10.1080/00949658508810853.

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Covino, Roberto, Pilar Cossio und Lars Dingeldein. „Simulation-based inference of single-molecule force spectroscopy“. Biophysical Journal 122, Nr. 3 (Februar 2023): 140a. http://dx.doi.org/10.1016/j.bpj.2022.11.920.

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Hur, YoonHaeng, Wenxuan Guo und Tengyuan Liang. „Reversible Gromov–Monge Sampler for Simulation-Based Inference“. SIAM Journal on Mathematics of Data Science 6, Nr. 2 (05.04.2024): 283–310. http://dx.doi.org/10.1137/23m1550384.

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Rossman, Allan J., und Beth L. Chance. „Using simulation-based inference for learning introductory statistics“. Wiley Interdisciplinary Reviews: Computational Statistics 6, Nr. 4 (26.05.2014): 211–21. http://dx.doi.org/10.1002/wics.1302.

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Graber, Vanessa, Michele Ronchi, Celsa Pardo-Araujo und Nanda Rea. „Isolated Pulsar Population Synthesis with Simulation-based Inference“. Astrophysical Journal 968, Nr. 1 (01.06.2024): 16. http://dx.doi.org/10.3847/1538-4357/ad3e78.

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Abstract We combine pulsar population synthesis with simulation-based inference (SBI) to constrain the magnetorotational properties of isolated Galactic radio pulsars. We first develop a framework to model neutron star birth properties and their dynamical and magnetorotational evolution. We specifically sample initial magnetic field strengths, B, and spin periods, P, from lognormal distributions and capture the late-time magnetic field decay with a power law. Each lognormal is described by a mean, μ log B , μ log P , and standard deviation, σ log B , σ log P , while the power law is characterized by the index, a late. We subsequently model the stars’ radio emission and observational biases to mimic detections with three radio surveys, and we produce a large database of synthetic P– P ̇ diagrams by varying our five magnetorotational input parameters. We then follow an SBI approach that focuses on neural posterior estimation and train deep neural networks to infer the parameters’ posterior distributions. After successfully validating these individual neural density estimators on simulated data, we use an ensemble of networks to infer the posterior distributions for the observed pulsar population. We obtain μ log B = 13.10 − 0.10 + 0.08 , σ log B = 0.45 − 0.05 + 0.05 and μ log P = − 1.00 − 0.21 + 0.26 , σ log P = 0.38 − 0.18 + 0.33 for the lognormal distributions and a late = − 1.80 − 0.61 + 0.65 for the power law at the 95% credible interval. We contrast our results with previous studies and highlight uncertainties of the inferred a late value. Our approach represents a crucial step toward robust statistical inference for complex population synthesis frameworks and forms the basis for future multiwavelength analyses of Galactic pulsars.

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Andersen, Torben G. „SIMULATION-BASED ECONOMETRIC METHODS“. Econometric Theory 16, Nr. 1 (Februar 2000): 131–38. http://dx.doi.org/10.1017/s0266466600001080.

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The accessibility of high-performance computing power has always influenced theoretical and applied econometrics. Gouriéroux and Monfort begin their recent offering, Simulation-Based Econometric Methods, with a stylized three-stage classification of the history of statistical econometrics. In the first stage, lasting through the 1960's, models and estimation methods were designed to produce closed-form expressions for the estimators. This spurred thorough investigation of the standard linear model, linear simultaneous equations with the associated instrumental variable techniques, and maximum likelihood estimation within the exponential family. During the 1970's and 1980's the development of powerful numerical optimization routines led to the exploration of procedures without closed-form solutions for the estimators. During this period the general theory of nonlinear statistical inference was developed, and nonlinear micro models such as limited dependent variable models and nonlinear time series models, e.g., ARCH, were explored. The associated estimation principles included maximum likelihood (beyond the exponential family), pseudo-maximum likelihood, nonlinear least squares, and generalized method of moments. Finally, the third stage considers problems without a tractable analytic criterion function. Such problems almost invariably arise from the need to evaluate high-dimensional integrals. The idea is to circumvent the associated numerical problems by a simulation-based approach. The main requirement is therefore that the model may be simulated given the parameters and the exogenous variables. The approach delivers simulated counterparts to standard estimation procedures and has inspired the development of entirely new procedures based on the principle of indirect inference.

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CHANCE, BETH, NATHAN TINTLE, SHEA REYNOLDS, AJAY PATEL, KATHERINE CHAN und SEAN LEADER. „STUDENT PERFORMANCE IN CURRICULA CENTERED ON SIMULATION-BASED INFERENCE“. STATISTICS EDUCATION RESEARCH JOURNAL 21, Nr. 3 (01.12.2022): 4. http://dx.doi.org/10.52041/serj.v21i3.6.

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Using simulation-based inference (SBI), such as randomization tests, as the primary vehicle for introducing students to the logic and scope of statistical inference has been advocated with the potential of improving student understanding of statistical inference and the statistical investigative process as a whole. Moving beyond the individual class activity, entirely revised introductory statistics curricula centering on these ideas have been developed and tested. Preliminary assessment data have been largely positive. In this paper, we discuss three years of cross-institutional tertiary-level data from the United States comparing SBI-focused curricula and non-SBI curricula (86 distinct institutions). We examined several pre/post measures of conceptual understanding in the introductory algebra-based course using multi-level modelling to incorporate student-level, instructor-level, and institutional-level covariates. We found that pre-course student characteristics (e.g., prior knowledge) were the strongest predictors of student learning, but also that textbook choice can still have a meaningful impact on student understanding of key statistical concepts. In particular, textbook choice was the strongest “modifiable” predictor of student outcomes of those examined, with simulation-based inference texts yielding the largest changes in student learning outcomes. Further research is needed to elucidate the particular aspects of SBI curricula that contribute to observed student learning gains.

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Tucci, Beatriz, und Fabian Schmidt. „EFTofLSS meets simulation-based inference: σ ₈ from biased tracers“. Journal of Cosmology and Astroparticle Physics 2024, Nr. 05 (01.05.2024): 063. http://dx.doi.org/10.1088/1475-7516/2024/05/063.

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Abstract Cosmological inferences typically rely on explicit expressions for the likelihood and covariance of the data vector, which normally consists of a set of summary statistics. However, in the case of nonlinear large-scale structure, exact expressions for either likelihood or covariance are unknown, and even approximate expressions can become very cumbersome, depending on the scales and summary statistics considered. Simulation-based inference (SBI), in contrast, does not require an explicit form for the likelihood but only a prior and a simulator, thereby naturally circumventing these issues. In this paper, we explore how this technique can be used to infer σ 8 from a Lagrangian effective field theory (EFT) based forward model for biased tracers. The power spectrum and bispectrum are used as summary statistics to obtain the posterior of the cosmological, bias and noise parameters via neural density estimation. We compare full simulation-based inference with cases where the data vector is drawn from a Gaussian likelihood with sample and analytical covariances. We conclude that, for k max = 0.1hMpc-1 and 0.2hMpc-1, the form of the covariance is more important than the non-Gaussianity of the likelihood, although this conclusion is expected to depend on the cosmological parameter inferred, the summary statistics considered and range of scales probed.

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Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton und Kenji Fukumizu. „Model-based kernel sum rule: kernel Bayesian inference with probabilistic models“. Machine Learning 109, Nr. 5 (02.01.2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.

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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.

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Makinen, T. Lucas, Tom Charnock, Justin Alsing und Benjamin D. Wandelt. „Lossless, scalable implicit likelihood inference for cosmological fields“. Journal of Cosmology and Astroparticle Physics 2021, Nr. 11 (01.11.2021): 049. http://dx.doi.org/10.1088/1475-7516/2021/11/049.

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Abstract We present a comparison of simulation-based inference to full, field-based analytical inference in cosmological data analysis. To do so, we explore parameter inference for two cases where the information content is calculable analytically: Gaussian random fields whose covariance depends on parameters through the power spectrum; and correlated lognormal fields with cosmological power spectra. We compare two inference techniques: i) explicit field-level inference using the known likelihood and ii) implicit likelihood inference with maximally informative summary statistics compressed via Information Maximising Neural Networks (IMNNs). We find that a) summaries obtained from convolutional neural network compression do not lose information and therefore saturate the known field information content, both for the Gaussian covariance and the lognormal cases, b) simulation-based inference using these maximally informative nonlinear summaries recovers nearly losslessly the exact posteriors of field-level inference, bypassing the need to evaluate expensive likelihoods or invert covariance matrices, and c) even for this simple example, implicit, simulation-based likelihood incurs a much smaller computational cost than inference with an explicit likelihood. This work uses a new IMNN implementation in Jax that can take advantage of fully-differentiable simulation and inference pipeline. We also demonstrate that a single retraining of the IMNN summaries effectively achieves the theoretically maximal information, enhancing the robustness to the choice of fiducial model where the IMNN is trained.

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Kulkarni, Sourabh, Mario Michael Krell, Seth Nabarro und Csaba Andras Moritz. „Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19“. ACM Journal on Emerging Technologies in Computing Systems 18, Nr. 2 (30.04.2022): 1–24. http://dx.doi.org/10.1145/3471188.

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Epidemiology models are central to understanding and controlling large-scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel’s Xeon CPU, NVIDIA’s Tesla V100 GPU, Google’s V2 Tensor Processing Unit (TPU), and the Graphcore’s Mk1 Intelligence Processing Unit (IPU), and the results are discussed in the context of their computational architectures. Results show that TPUs are 3×, GPUs are 4×, and IPUs are 30× faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries and giving a brief overview of the results.

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Nickl, R., und B. M. Pötscher. „Efficient simulation-based minimum distance estimation and indirect inference“. Mathematical Methods of Statistics 19, Nr. 4 (Dezember 2010): 327–64. http://dx.doi.org/10.3103/s1066530710040022.

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Koo, Sung-Han. „Forecasting Fire Risk Using Simulation Based on Bayesian Inference“. Journal of The Korean Society of Living Environmental System 22, Nr. 2 (30.04.2015): 189. http://dx.doi.org/10.21086/ksles.2015.04.22.2.189.

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Dinov, Ivo D., Selvam Palanimalai, Ashwini Khare und Nicolas Christou. „Randomization-based statistical inference: A resampling and simulation infrastructure“. Teaching Statistics 40, Nr. 2 (11.04.2018): 64–73. http://dx.doi.org/10.1111/test.12156.

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Sharma, Abhishek, und Keith M. Goolsbey. „Simulation-Based Approach to Efficient Commonsense Reasoning in Very Large Knowledge Bases“. Proceedings of the AAAI Conference on Artificial Intelligence 33 (17.07.2019): 1360–67. http://dx.doi.org/10.1609/aaai.v33i01.33011360.

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Cognitive systems must reason with large bodies of general knowledge to perform complex tasks in the real world. However, due to the intractability of reasoning in large, expressive knowledge bases (KBs), many AI systems have limited reasoning capabilities. Successful cognitive systems have used a variety of machine learning and axiom selection methods to improve inference. In this paper, we describe a search heuristic that uses a Monte-Carlo simulation technique to choose inference steps. We test the efficacy of this approach on a very large and expressive KB, Cyc. Experimental results on hundreds of queries show that this method is highly effective in reducing inference time and improving question-answering (Q/A) performance.

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Nguyen, Minh, Roly Perera, Meng Wang und Nicolas Wu. „Modular probabilistic models via algebraic effects“. Proceedings of the ACM on Programming Languages 6, ICFP (29.08.2022): 381–410. http://dx.doi.org/10.1145/3547635.

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Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers.

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Guo, Yin Zhang, und Jian Chao Zeng. „Temporal Inference and Simulation in Collaborative Design Process Based on Time Petri Net“. Applied Mechanics and Materials 34-35 (Oktober 2010): 85–91. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.85.

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Complex manufacture collaborative design process is a dynamic process with uncertainty, which is a process with multi-task executing in a parallel manner. It has obviously time constraint, which has been few studied up to now. Temporary inference and simulation problem for complex manufacture design process are mainly studied in this paper. Firstly, a Petri net model for collaborative design process is proposed based on time Petri net theory and four temporary inference algorithms of ordering, paralleling, selecting and circling structures in collaborative design process. Secondly, the authors design and develop a visually modeling and simulating circumstance for collaborative design process temporary inference based on time Petri net. Temporary inference for the whole collaborative design process and optimization to design task can be implemented by this simulation software. Finally, collaborative design process for driver of chain-transportation-driven equipment is undertaken to shown the efficiency of the proposed method. Research in this paper affords a determinant principle for time arrangement of collaborative design task and it also affords schema scheduling.

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Barret, Didier, und Simon Dupourqué. „Simulation-based inference with neural posterior estimation applied to X-ray spectral fitting“. Astronomy & Astrophysics 686 (Juni 2024): A133. http://dx.doi.org/10.1051/0004-6361/202449214.

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Context. Neural networks are being extensively used for modeling data, especially in the case where no likelihood can be formulated. Aims. Although in the case of X-ray spectral fitting the likelihood is known, we aim to investigate the ability of neural networks to recover the model parameters and their associated uncertainties and to compare their performances with standard X-ray spectral fitting, whether following a frequentist or Bayesian approach. Methods. We applied a simulation-based inference with neural posterior estimation (SBI-NPE) to X-ray spectra. We trained a network with simulated spectra generated from a multiparameter source emission model folded through an instrument response, so that it learns the mapping between the simulated spectra and their parameters and returns the posterior distribution. The model parameters are sampled from a predefined prior distribution. To maximize the efficiency of the training of the neural network, while limiting the size of the training sample to speed up the inference, we introduce a way to reduce the range of the priors, either through a classifier or a coarse and quick inference of one or multiple observations. For the sake of demonstrating working principles, we applied the technique to data generated from and recorded by the NICER X-ray instrument, which is a medium-resolution X-ray spectrometer covering the 0.2–12 keV band. We consider here simple X-ray emission models with up to five parameters. Results. SBI-NPE is demonstrated to work equally well as standard X-ray spectral fitting, both in the Gaussian and Poisson regimes, on simulated and real data, yielding fully consistent results in terms of best-fit parameters and posterior distributions. The inference time is comparable to or smaller than the one needed for Bayesian inference when involving the computation of large Markov chain Monte Carlo chains to derive the posterior distributions. On the other hand, once properly trained, an amortized SBI-NPE network generates the posterior distributions in no time (less than 1 second per spectrum on a 6-core laptop). We show that SBI-NPE is less sensitive to local minima trapping than standard fit statistic minimization techniques. With a simple model, we find that the neural network can be trained equally well on dimension-reduced spectra via a principal component decomposition, leading to a faster inference time with no significant degradation of the posteriors. Conclusions. We show that simulation-based inference with neural posterior estimation is a complementary tool for X-ray spectral analysis. The technique is robust and produces well-calibrated posterior distributions. It holds great potential for its integration in pipelines developed for processing large data sets. The code developed to demonstrate the first working principles of the technique introduced here is released through a Python package called SIXSA (Simulation-based Inference for X-ray Spectral Analysis), which is available from GitHub.

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Diop, Aba, Aliou Diop und Jean-François Dupuy. „Simulation-based Inference in a Zero-inflated Bernoulli Regression Model“. Communications in Statistics - Simulation and Computation 45, Nr. 10 (13.11.2014): 3597–614. http://dx.doi.org/10.1080/03610918.2014.950743.

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Contoyannis, Paul, Andrew M. Jones und Roberto Leon-Gonzalez. „Using simulation-based inference with panel data in health economics“. Health Economics 13, Nr. 2 (16.01.2004): 101–22. http://dx.doi.org/10.1002/hec.811.

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Gupta, Revant, Dario Cerletti, Gilles Gut, Annette Oxenius und Manfred Claassen. „Simulation-based inference of differentiation trajectories from RNA velocity fields“. Cell Reports Methods 2, Nr. 12 (Dezember 2022): 100359. http://dx.doi.org/10.1016/j.crmeth.2022.100359.

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Wang, Zijian, Jan Hasenauer und Yannik Schälte. „Missing data in amortized simulation-based neural posterior estimation“. PLOS Computational Biology 20, Nr. 6 (17.06.2024): e1012184. http://dx.doi.org/10.1371/journal.pcbi.1012184.

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Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates. In this work, we discuss various ways of encoding missing data and integrate them into the training and inference process. We implement the approaches in the BayesFlow methodology, an amortized estimation framework based on invertible neural networks, and evaluate their performance on multiple test problems. We find that an approach in which the data vector is augmented with binary indicators of presence or absence of values performs the most robustly. Indeed, it improved the performance also for the simpler problem of data sets with variable length. Accordingly, we demonstrate that amortized simulation-based inference approaches are applicable even with missing data, and we provide a guideline for their handling, which is relevant for a broad spectrum of applications.

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Tu, Zhihao, Jiehong Wu, Xin Huang, Ruei-Yuan Wang und Ho-Sheng Chen. „The Study of Numerical Simulation Based on Fuzzy PID Controller“. International Journal of Advanced Engineering Research and Science 10, Nr. 10 (2023): 108–13. http://dx.doi.org/10.22161/ijaers.1010.10.

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This study aims to explore the application of an adaptive fuzzy PID (proportional integral differential) controller in system control. This controller combines the characteristics of fuzzy control and PID control and dynamically adjusts the parameters of the PID controller through a fuzzy inference mechanism to achieve adaptive adjustment of the system's dynamic characteristics. Firstly, by taking the control object of an industrial process as an example, the transfer function of the controlled object is determined to determine the initial parameters of the PID controller. Subsequently, a fuzzy inference module was introduced to adjust the proportional, integral, and differential coefficients of the PID controller through fuzzy rules based on the current state and error situation of the system. The simulation results show that, compared to traditional PID controllers, the adaptive fuzzy PID controller has achieved significant improvements in dynamic response speed and stability. Especially in the face of complex and rapidly changing control systems.

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Avecilla, Grace, Julie N. Chuong, Fangfei Li, Gavin Sherlock, David Gresham und Yoav Ram. „Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics“. PLOS Biology 20, Nr. 5 (27.05.2022): e3001633. http://dx.doi.org/10.1371/journal.pbio.3001633.

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The rate of adaptive evolution depends on the rate at which beneficial mutations are introduced into a population and the fitness effects of those mutations. The rate of beneficial mutations and their expected fitness effects is often difficult to empirically quantify. As these 2 parameters determine the pace of evolutionary change in a population, the dynamics of adaptive evolution may enable inference of their values. Copy number variants (CNVs) are a pervasive source of heritable variation that can facilitate rapid adaptive evolution. Previously, we developed a locus-specific fluorescent CNV reporter to quantify CNV dynamics in evolving populations maintained in nutrient-limiting conditions using chemostats. Here, we use CNV adaptation dynamics to estimate the rate at which beneficial CNVs are introduced through de novo mutation and their fitness effects using simulation-based likelihood–free inference approaches. We tested the suitability of 2 evolutionary models: a standard Wright–Fisher model and a chemostat model. We evaluated 2 likelihood-free inference algorithms: the well-established Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) algorithm, and the recently developed Neural Posterior Estimation (NPE) algorithm, which applies an artificial neural network to directly estimate the posterior distribution. By systematically evaluating the suitability of different inference methods and models, we show that NPE has several advantages over ABC-SMC and that a Wright–Fisher evolutionary model suffices in most cases. Using our validated inference framework, we estimate the CNV formation rate at the GAP1 locus in the yeast Saccharomyces cerevisiae to be 10−4.7 to 10−4 CNVs per cell division and a fitness coefficient of 0.04 to 0.1 per generation for GAP1 CNVs in glutamine-limited chemostats. We experimentally validated our inference-based estimates using 2 distinct experimental methods—barcode lineage tracking and pairwise fitness assays—which provide independent confirmation of the accuracy of our approach. Our results are consistent with a beneficial CNV supply rate that is 10-fold greater than the estimated rates of beneficial single-nucleotide mutations, explaining the outsized importance of CNVs in rapid adaptive evolution. More generally, our study demonstrates the utility of novel neural network–based likelihood–free inference methods for inferring the rates and effects of evolutionary processes from empirical data with possible applications ranging from tumor to viral evolution.

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Cottin, Aurélien, Benjamin Penaud, Jean-Christophe Glaszmann, Nabila Yahiaoui und Mathieu Gautier. „Simulation-Based Evaluation of Three Methods for Local Ancestry Deconvolution of Non-model Crop Species Genomes“. G3: Genes|Genomes|Genetics 10, Nr. 2 (20.12.2019): 569–79. http://dx.doi.org/10.1534/g3.119.400873.

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Hybridizations between species and subspecies represented major steps in the history of many crop species. Such events generally lead to genomes with mosaic patterns of chromosomal segments of various origins that may be assessed by local ancestry inference methods. However, these methods have mainly been developed in the context of human population genetics with implicit assumptions that may not always fit plant models. The purpose of this study was to evaluate the suitability of three state-of-the-art inference methods (SABER, ELAI and WINPOP) for local ancestry inference under scenarios that can be encountered in plant species. For this, we developed an R package to simulate genotyping data under such scenarios. The tested inference methods performed similarly well as far as representatives of source populations were available. As expected, the higher the level of differentiation between ancestral source populations and the lower the number of generations since admixture, the more accurate were the results. Interestingly, the accuracy of the methods was only marginally affected by i) the number of ancestries (up to six tested); ii) the sample design (i.e., unbalanced representation of source populations); and iii) the reproduction mode (e.g., selfing, vegetative propagation). If a source population was not represented in the data set, no bias was observed in inference accuracy for regions originating from represented sources and regions from the missing source were assigned differently depending on the methods. Overall, the selected ancestry inference methods may be used for crop plant analysis if all ancestral sources are known.

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Chen, Ziqi, Kirill V. Horoshenkov und Ning Xiang. „Bayesian inference for boundary admittance estimation using a multipole model for room-acoustic simulation“. Journal of the Acoustical Society of America 150, Nr. 4 (Oktober 2021): A348. http://dx.doi.org/10.1121/10.0008540.

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Acoustic surface admittance/impedance at room boundaries is essential for wave-based room-acoustic simulations. In this work, two levels of Bayesian inference are applied to estimate the surface admittance based on a multipole admittance model. This work estimates the order of the multipole admittance model through the high level of inference, Bayesian model selection. The first (low) level of inference, Bayesian parameter estimation, is applied to estimate the parameter values of the surface admittance model once model order is selected. This work approximates the frequency-dependent admittance from experimentally measured a set of acoustic surface admittance data. Analysis results demonstrate that multipole model-based Bayesian inference is well suited in estimating the frequency-dependent boundary condition within wave-based simulation framework. Numerical simulations verify the estimation results of Bayesian inference.

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Zhang, Jun, Jia Yu, Tao Guan, Jiajun Wang, Dawei Tong und Binping Wu. „Adaptive Compaction Construction Simulation Based on Bayesian Field Theory“. Sensors 20, Nr. 18 (10.09.2020): 5178. http://dx.doi.org/10.3390/s20185178.

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The compaction construction process is a critical operation in civil engineering projects. By establishing a construction simulation model, the compaction duration can be predicted to assist construction management. Existing studies have achieved adaptive modelling of input parameters from a Bayesian inference perspective, but usually assume the model as parametric distribution. Few studies adopt the nonparametric distribution to achieve robust inference, but still need to manually set hyper-parameters. In addition, the condition of when the roller stops moving ignores the impact of randomness of roller movement. In this paper, a new adaptive compaction construction simulation method is presented. The Bayesian field theory is innovatively adopted for input parameter adaptive modelling. Next, whether rollers have offset enough distance is used to determine the moment of stopping. Simulation experiments of the compaction process of a high earth dam project are demonstrated. The results indicate that the Bayesian field theory performs well in terms of accuracy and efficiency. When the size of roller speed dataset is 787,490, the Bayesian field theory costs only 1.54 s. The mean absolute error of predicted compaction duration reduces significantly with improved judgment condition. The proposed method can contribute to project resource planning, particularly in a high-frequency construction monitoring environment.

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KAZAK, SIBEL, TARO FUJITA und RUPERT WEGERIF. „STUDENTS’ INFORMAL INFERENCE ABOUT THE BINOMIAL DISTRIBUTION OF “BUNNY HOPS”: A DIALOGIC PERSPECTIVE“. STATISTICS EDUCATION RESEARCH JOURNAL 15, Nr. 2 (30.11.2016): 46–61. http://dx.doi.org/10.52041/serj.v15i2.240.

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The study explores the development of 11-year-old students’ informal inference about random bunny hops through student talk and use of computer simulation tools. Our aim in this paper is to draw on dialogic theory to explain how students make shifts in perspective, from intuition-based reasoning to more powerful, formal ways of using probabilistic ideas. Findings from the study suggest that dialogic talk facilitated students’ reasoning as it was supported by the use of simulation tools available in the software. It appears that the interaction of using simulation tools, talk between students, and teacher prompts helps students develop their understanding of probabilistic ideas in the context of making inferences about the distribution of random bunny hops. First published November 2016 at Statistics Education Research Journal Archives

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Parody, Robert J., und Don Edwards. „Simulation-Based Inference on the Improvement in a Rotatable Response Surface“. Quality Technology & Quantitative Management 4, Nr. 4 (Januar 2007): 489–99. http://dx.doi.org/10.1080/16843703.2007.11673167.

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Lawless, J. F., und Denise Babineau. „Models for interval censoring and simulation-based inference for lifetime distributions“. Biometrika 93, Nr. 3 (01.09.2006): 671–86. http://dx.doi.org/10.1093/biomet/93.3.671.

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Li, Mingyang, und Zequn Wang. „Heterogeneous uncertainty quantification using Bayesian inference for simulation-based design optimization“. Structural Safety 85 (Juli 2020): 101954. http://dx.doi.org/10.1016/j.strusafe.2020.101954.

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Wang, Wenjing, Nan Chen, Xi Chen und Linchang Yang. „A Variational Inference-Based Heteroscedastic Gaussian Process Approach for Simulation Metamodeling“. ACM Transactions on Modeling and Computer Simulation 29, Nr. 1 (23.02.2019): 1–22. http://dx.doi.org/10.1145/3299871.

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Verdier, Hippolyte, François Laurent, Alhassan Cassé, Christian L. Vestergaard, Christian G. Specht und Jean-Baptiste Masson. „Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics“. PLOS Computational Biology 19, Nr. 2 (02.02.2023): e1010088. http://dx.doi.org/10.1371/journal.pcbi.1010088.

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Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules. Recorded trajectories are often too short to reliably discern such multi-scale dynamics, which precludes unambiguous assessment of the type of random walk and its parameters. Furthermore, the motion of biomolecules may not be well described by a single, canonical random walk model. Here, we develop a two-step statistical testing scheme for comparing biomolecule dynamics observed in different experimental conditions without having to identify or make strong prior assumptions about the model generating the recorded random walks. We first train a graph neural network to perform simulation-based inference and thus learn a rich summary statistics vector describing individual trajectories. We then compare trajectories obtained in different biological conditions using a non-parametric maximum mean discrepancy (MMD) statistical test on their so-obtained summary statistics. This procedure allows us to characterise sets of random walks regardless of their generating models, without resorting to model-specific physical quantities or estimators. We first validate the relevance of our approach on numerically simulated trajectories. This demonstrates both the statistical power of the MMD test and the descriptive power of the learnt summary statistics compared to estimates of physical quantities. We then illustrate the ability of our framework to detect changes in α-synuclein dynamics at synapses in cultured cortical neurons, in response to membrane depolarisation, and show that detected differences are largely driven by increased protein mobility in the depolarised state, in agreement with previous findings. The method provides a means of interpreting the differences it detects in terms of single trajectory characteristics. Finally, we emphasise the interest of performing various comparisons to probe the heterogeneity of experimentally acquired datasets at different levels of granularity (e.g., biological replicates, fields of view, and organelles).

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Winker, Peter, Manfred Gilli und Vahidin Jeleskovic. „An objective function for simulation based inference on exchange rate data“. Journal of Economic Interaction and Coordination 2, Nr. 2 (15.06.2007): 125–45. http://dx.doi.org/10.1007/s11403-007-0020-4.

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Lu, Jiannan. „Sharpening randomization-based causal inference for 22 factorial designs with binary outcomes“. Statistical Methods in Medical Research 28, Nr. 4 (05.12.2017): 1064–78. http://dx.doi.org/10.1177/0962280217745720.

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In medical research, a scenario often entertained is randomized controlled 22 factorial design with a binary outcome. By utilizing the concept of potential outcomes, Dasgupta et al. proposed a randomization-based causal inference framework, allowing flexible and simultaneous estimations and inferences of the factorial effects. However, a fundamental challenge that Dasgupta et al.’s proposed methodology faces is that the sampling variance of the randomization-based factorial effect estimator is unidentifiable, rendering the corresponding classic “Neymanian” variance estimator suffering from over-estimation. To address this issue, for randomized controlled 22 factorial designs with binary outcomes, we derive the sharp lower bound of the sampling variance of the factorial effect estimator, which leads to a new variance estimator that sharpens the finite-population Neymanian causal inference. We demonstrate the advantages of the new variance estimator through a series of simulation studies, and apply our newly proposed methodology to two real-life datasets from randomized clinical trials, where we gain new insights.

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Liang, Xin Rong, und Di Qian Wang. „Design and Simulation of Speed Limit Controller Based on Fuzzy Logic Inference“. Applied Mechanics and Materials 220-223 (November 2012): 988–91. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.988.

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Speed limit control is of great importance in freeway traffic control. This study aims to determine the reasonable speed limit through the application of a fuzzy inference method. Firstly, the components of a fuzzy logic controller are formulated. Then the influential factors of speed limit control are analyzed and the speed limit controller based on fuzzy logic is designed according to such information as the number of vehicles on the freeway, the road grade, and the weather conditions. Gauss, g-bell and triangle curves are used for the membership functions of the fuzzy variables. The rule base including 45 fuzzy rules is also established. Finally, the speed limit controller is simulated. Simulation results show that the speed limit values are reasonable. Fuzzy inference method provides a novel and practical way to realize speed limit control.

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Zhu, Hai Ting, Wei Ding und Jun Hui Ni. „A New Virus Source Inference Model Based on Network Performance Estimation“. Applied Mechanics and Materials 433-435 (Oktober 2013): 1693–98. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1693.

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Combining network tomography, a new virus source inference model based on network performance is proposed. Both the topology information and real time network status are considered in our model. Performance metrics are introduced into rumor-centrality-based source detecting algorithm in an active inference way. By improving the process of setting up the spanning tree of infected topology we raise the virus source inference precision and reduce the time complexity of rumor-centrality-based algorithm from O(N2(|V|+|E|)) to O(N2). The simulation results show that our model achieves better estimation accuracy than the algorithm using rumor center as the estimator.

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Korovkina, Marina Ye, und Arkady L. Semenov. „Inferencing and Functional Approach to Text: Based on Simultaneous Interpreting“. RUDN Journal of Language Studies, Semiotics and Semantics 13, Nr. 2 (14.07.2022): 337–52. http://dx.doi.org/10.22363/2313-2299-2022-13-2-337-352.

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A prompt exchange of information requires a thrift in expressive means and makes relevant the processes of verbalizing and reverbalizing sense in bilingual communication. The analysis of various communicative situations in simultaneous interpreting both from English into Russian and Russian into English show specific parameters of generating a text in the target language, which differ from the traditional author’s text generation. The study’s objective is to introduce the notion of inferencing (in the Russian linguistic studies the term inference is used) as a special linguistic tool of eliciting and interpreting sense in the process of translation/interpreting. General linguistic understanding of inference and implication refers to the direct communication between a sender (speaker) and recipient (listener) that produces interrelated notions via independent inferences. The method of sampling and comparing the inferences accumulated empirically and of simulation modelling of simultaneous interpreting can demonstrate that a distinctive feature of indirect communication via interpreter is multi-component system of additional cognitive procedures at the stage of interpreting. The simultaneous interpreting outcomes as the material of research indicate that conveying the sense in interpreting is based on a dynamic process of inferencing, which is a creative search of implicatures and their reverbalization, with an interpreter resorting to his/ her own thesaurus-based resources (mental lexicon) related to the knowledge of domain, which is a subject of communication. The distinctive parameter of inferencing in interpreting is both the generation of inferences and implicatures and a cognitive analytical process of selecting facts from presuppositional knowledge. The study outcomes show that the cognitive analytical process of inferencing is closely related to probabilistic forecasting, which presupposes an accumulation by an interpreter of a sufficient individual base of verified intuitive solutions and referential languagebased interpretations and presuppositions. The study makes a conclusion that of crucial importance for interpreting is inferencing, rather than inference as such.

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