Journal articles: 'Nested inference'

1

Torvik, V. "Guided inference of nested monotone Boolean functions." Information Sciences 151 (May 2003): 171–200. http://dx.doi.org/10.1016/s0020-0255(03)00062-8.

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McAleer, Michael, and M. Hashem Pesaran. "Statistical inference in non-nested econometric models." Applied Mathematics and Computation 20, no. 3-4 (November 1986): 271–311. http://dx.doi.org/10.1016/0096-3003(86)90008-1.

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Shao, Fang, Jialiang Li, Jason Fine, Weng Kee Wong, and Michael Pencina. "Inference for reclassification statistics under nested and non-nested models for biomarker evaluation." Biomarkers 20, no. 4 (May 19, 2015): 240–52. http://dx.doi.org/10.3109/1354750x.2015.1068854.

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Powell, Sean, Kristoffer Forslund, Damian Szklarczyk, Kalliopi Trachana, Alexander Roth, Jaime Huerta-Cepas, Toni Gabaldón, et al. "eggNOG v4.0: nested orthology inference across 3686 organisms." Nucleic Acids Research 42, no. D1 (December 1, 2013): D231—D239. http://dx.doi.org/10.1093/nar/gkt1253.

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Cribari-Neto, Francisco, and Sadraque E. F. Lucena. "Non-nested hypothesis testing inference for GAMLSS models." Journal of Statistical Computation and Simulation 87, no. 6 (November 14, 2016): 1189–205. http://dx.doi.org/10.1080/00949655.2016.1255946.

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Li, Heng, and Hal S. Stern. "Bayesian Inference for Nested Designs Based on Jeffreys's Prior." American Statistician 51, no. 3 (August 1997): 219. http://dx.doi.org/10.2307/2684891.

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Li, Heng, and Hal S. Stern. "Bayesian Inference for Nested Designs Based on Jeffreys's Prior." American Statistician 51, no. 3 (August 1997): 219–24. http://dx.doi.org/10.1080/00031305.1997.10473966.

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Bussche, Jan Van den, and Stijn Vansummeren. "Polymorphic type inference for the named nested relational calculus." ACM Transactions on Computational Logic 9, no. 1 (December 2007): 3. http://dx.doi.org/10.1145/1297658.1297661.

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Ray, Anandaroop. "Bayesian inversion using nested trans-dimensional Gaussian processes." Geophysical Journal International 226, no. 1 (March 26, 2021): 302–26. http://dx.doi.org/10.1093/gji/ggab114.

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SUMMARY To understand earth processes, geoscientists infer subsurface earth properties such as electromagnetic resistivity or seismic velocity from surface observations of electromagnetic or seismic data. These properties are used to populate an earth model vector, and the spatial variation of properties across this vector sheds light on the underlying earth structure or physical phenomenon of interest, from groundwater aquifers to plate tectonics. However, to infer these properties the spatial characteristics of these properties need to be known in advance. Typically, assumptions are made about the length scales of earth properties, which are encoded a priori in a Bayesian probabilistic setting. In an optimization setting, appeals are made to promote model simplicity together with constraints which keep models close to a preferred model. All of these approaches are valid, though they can lead to unintended features in the resulting inferred geophysical models owing to inappropriate prior assumptions, constraints or even the nature of the solution basis functions. In this work it will be shown that in order to make accurate inferences about earth properties, inferences can first be made about the underlying length scales of these properties in a very general solution basis. From a mathematical point of view, these spatial characteristics of earth properties can be conveniently thought of as ‘properties’ of the earth properties. Thus, the same machinery used to infer earth properties can be used to infer their length scales. This can be thought of as an ‘infer to infer’ paradigm analogous to the ‘learning to learn’ paradigm which is now commonplace in the machine learning literature. However, it must be noted that (geophysical) inference is not the same as (machine) learning, though there are many common elements which allow for cross-pollination of useful ideas from one field to the other, as is shown here. A non-stationary trans-dimensional Gaussian Process (TDGP) is used to parametrize earth properties, and a multichannel stationary TDGP is used to parametrize the length scales associated with the earth property in question. Using non-stationary kernels, that is kernels with spatially variable length scales, models with sharp discontinuities can be represented within this framework. As GPs are multidimensional interpolators, the same theory and computer code can be used to solve geophysical problems in 1-D, 2-D and 3-D. This is demonstrated through a combination of 1-D and 2-D non-linear regression examples and a controlled source electromagnetic field example. The key difference between this and previous work using TDGP is generalized nested inference and the marginalization of prior length scales for better posterior subsurface property characterization.

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Autzen, Bengt. "BAYESIAN OCKHAM’S RAZOR AND NESTED MODELS." Economics and Philosophy 35, no. 02 (January 14, 2019): 321–38. http://dx.doi.org/10.1017/s0266267118000305.

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Abstract:While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham’s razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham’s razor is applied to nested economic models. I will argue that previous responses to the problem found in the philosophical literature are unsatisfactory and develop a novel reply to the problem.

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Russel, Patricio Maturana, Brendon J. Brewer, Steffen Klaere, and Remco R. Bouckaert. "Model Selection and Parameter Inference in Phylogenetics Using Nested Sampling." Systematic Biology 68, no. 2 (June 29, 2018): 219–33. http://dx.doi.org/10.1093/sysbio/syy050.

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Cai, Tianxi, and Yingye Zheng. "Resampling Procedures for Making Inference Under Nested Case–Control Studies." Journal of the American Statistical Association 108, no. 504 (December 2013): 1532–44. http://dx.doi.org/10.1080/01621459.2013.856715.

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Huckemann, Stephan F., and Benjamin Eltzner. "Backward nested descriptors asymptotics with inference on stem cell differentiation." Annals of Statistics 46, no. 5 (October 2018): 1994–2019. http://dx.doi.org/10.1214/17-aos1609.

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Chen, XinJie, YanQin Fan, Alan Wan, and GuoHua Zou. "Post-J test inference in non-nested linear regression models." Science China Mathematics 58, no. 6 (January 6, 2015): 1203–16. http://dx.doi.org/10.1007/s11425-014-4935-7.

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Yu, M., and J. N. K. Rao. "Inference for nested error linear regression models with unequal error variances." Journal of Statistical Planning and Inference 88, no. 2 (August 2000): 233–53. http://dx.doi.org/10.1016/s0378-3758(00)00081-1.

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Mikelson, Jan, and Mustafa Khammash. "Likelihood-free nested sampling for parameter inference of biochemical reaction networks." PLOS Computational Biology 16, no. 10 (October 9, 2020): e1008264. http://dx.doi.org/10.1371/journal.pcbi.1008264.

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TEMPLETON, ALAN R. "Nested clade analysis: an extensively validated method for strong phylogeographic inference." Molecular Ecology 17, no. 8 (March 10, 2008): 1877–80. http://dx.doi.org/10.1111/j.1365-294x.2008.03731.x.

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Hsiao, Chuhsing Kate, Miao-Yu Tsai, and Ho-Min Chen. "Inference of nested variance components in a longitudinal myopia intervention trial." Statistics in Medicine 24, no. 21 (2005): 3251–67. http://dx.doi.org/10.1002/sim.2211.

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周, 梦雨. "Statistical Inference of Hierarchical Linear Regression Model Based on Nested Structure." Statistics and Application 10, no. 01 (2021): 173–82. http://dx.doi.org/10.12677/sa.2021.101017.

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Tan, Chuanqi, Wei Qiu, Mosha Chen, Rui Wang, and Fei Huang. "Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9016–23. http://dx.doi.org/10.1609/aaai.v34i05.6434.

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Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is usually difficult to detect entities with nested structures. The span-based method that can easily detect nested entities in different subsequences is naturally suitable for the nested NER problem. However, previous span-based methods have two main issues. First, classifying all subsequences is computationally expensive and very inefficient at inference. Second, the span-based methods mainly focus on learning span representations but lack of explicit boundary supervision. To tackle the above two issues, we propose a boundary enhanced neural span classification model. In addition to classifying the span, we propose incorporating an additional boundary detection task to predict those words that are boundaries of entities. The two tasks are jointly trained under a multitask learning framework, which enhances the span representation with additional boundary supervision. In addition, the boundary detection model has the ability to generate high-quality candidate spans, which greatly reduces the time complexity during inference. Experiments show that our approach outperforms all existing methods and achieves 85.3, 83.9, and 78.3 scores in terms of F1 on the ACE2004, ACE2005, and GENIA datasets, respectively.

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Wasserman, Larry, Aaditya Ramdas, and Sivaraman Balakrishnan. "Universal inference." Proceedings of the National Academy of Sciences 117, no. 29 (July 6, 2020): 16880–90. http://dx.doi.org/10.1073/pnas.1922664117.

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We propose a general method for constructing confidence sets and hypothesis tests that have finite-sample guarantees without regularity conditions. We refer to such procedures as “universal.” The method is very simple and is based on a modified version of the usual likelihood-ratio statistic that we call “the split likelihood-ratio test” (split LRT) statistic. The (limiting) null distribution of the classical likelihood-ratio statistic is often intractable when used to test composite null hypotheses in irregular statistical models. Our method is especially appealing for statistical inference in these complex setups. The method we suggest works for any parametric model and also for some nonparametric models, as long as computing a maximum-likelihood estimator (MLE) is feasible under the null. Canonical examples arise in mixture modeling and shape-constrained inference, for which constructing tests and confidence sets has been notoriously difficult. We also develop various extensions of our basic methods. We show that in settings when computing the MLE is hard, for the purpose of constructing valid tests and intervals, it is sufficient to upper bound the maximum likelihood. We investigate some conditions under which our methods yield valid inferences under model misspecification. Further, the split LRT can be used with profile likelihoods to deal with nuisance parameters, and it can also be run sequentially to yield anytime-valid P values and confidence sequences. Finally, when combined with the method of sieves, it can be used to perform model selection with nested model classes.

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Tanaka, Shiro, M. Alan Brookhart, and Jason P. Fine. "G-estimation of structural nested mean models for competing risks data using pseudo-observations." Biostatistics 21, no. 4 (May 6, 2019): 860–75. http://dx.doi.org/10.1093/biostatistics/kxz015.

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Summary This article provides methods of causal inference for competing risks data. The methods are formulated as structural nested mean models of causal effects directly related to the cumulative incidence function or subdistribution hazard, which reflect the survival experience of a subject in the presence of competing risks. The effect measures include causal risk differences, causal risk ratios, causal subdistribution hazard ratios, and causal effects of time-varying exposures. Inference is implemented by g-estimation using pseudo-observations, a technique to handle censoring. The finite-sample performance of the proposed estimators in simulated datasets and application to time-varying exposures in a cohort study of type 2 diabetes are also presented.

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Gómez‐Rubio, Virgilio, and Francisco Palmí‐Perales. "Multivariate posterior inference for spatial models with the integrated nested Laplace approximation." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68, no. 1 (June 19, 2018): 199–215. http://dx.doi.org/10.1111/rssc.12292.

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Blei, David M., Thomas L. Griffiths, and Michael I. Jordan. "The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies." Journal of the ACM 57, no. 2 (January 2010): 1–30. http://dx.doi.org/10.1145/1667053.1667056.

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Pullen, Nick, and Richard J. Morris. "Bayesian Model Comparison and Parameter Inference in Systems Biology Using Nested Sampling." PLoS ONE 9, no. 2 (February 11, 2014): e88419. http://dx.doi.org/10.1371/journal.pone.0088419.

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Alsing, Justin, and Will Handley. "Nested sampling with any prior you like." Monthly Notices of the Royal Astronomical Society: Letters 505, no. 1 (June 14, 2021): L95—L99. http://dx.doi.org/10.1093/mnrasl/slab057.

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ABSTRACT Nested sampling is an important tool for conducting Bayesian analysis in Astronomy and other fields, both for sampling complicated posterior distributions for parameter inference, and for computing marginal likelihoods for model comparison. One technical obstacle to using nested sampling in practice is the requirement (for most common implementations) that prior distributions be provided in the form of transformations from the unit hyper-cube to the target prior density. For many applications – particularly when using the posterior from one experiment as the prior for another – such a transformation is not readily available. In this letter, we show that parametric bijectors trained on samples from a desired prior density provide a general purpose method for constructing transformations from the uniform base density to a target prior, enabling the practical use of nested sampling under arbitrary priors. We demonstrate the use of trained bijectors in conjunction with nested sampling on a number of examples from cosmology.

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Smith, Rory J. E., Gregory Ashton, Avi Vajpeyi, and Colm Talbot. "Massively parallel Bayesian inference for transient gravitational-wave astronomy." Monthly Notices of the Royal Astronomical Society 498, no. 3 (August 19, 2020): 4492–502. http://dx.doi.org/10.1093/mnras/staa2483.

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ABSTRACT Understanding the properties of transient gravitational waves (GWs) and their sources is of broad interest in physics and astronomy. Bayesian inference is the standard framework for astrophysical measurement in transient GW astronomy. Usually, stochastic sampling algorithms are used to estimate posterior probability distributions over the parameter spaces of models describing experimental data. The most physically accurate models typically come with a large computational overhead which can render data analsis extremely time consuming, or possibly even prohibitive. In some cases highly specialized optimizations can mitigate these issues, though they can be difficult to implement, as well as to generalize to arbitrary models of the data. Here, we investigate an accurate, flexible, and scalable method for astrophysical inference: parallelized nested sampling. The reduction in the wall-time of inference scales almost linearly with the number of parallel processes running on a high-performance computing cluster. By utilizing a pool of several hundreds or thousands of CPUs in a high-performance cluster, the large wall times of many astrophysical inferences can be alleviated while simultaneously ensuring that any GW signal model can be used ‘out of the box’, i.e. without additional optimization or approximation. Our method will be useful to both the LIGO-Virgo-KAGRA collaborations and the wider scientific community performing astrophysical analyses on GWs. An implementation is available in the open source gravitational-wave inference library pBilby (parallel bilby).

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PEYTON JONES, SIMON. "4 Declarations and Bindings." Journal of Functional Programming 13, no. 1 (January 2003): 39–66. http://dx.doi.org/10.1017/s0956796803000613.

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4.1 Overview of Types and Classes 404.2 User-Defined datatypes 454.3 Type Classes and Overloading 494.4 Nested Declarations 554.5 Static Semantics of Function and Pattern Bindings 604.6 Kind Inference 66

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Gómez-Rubio, Virgilio, Roger S. Bivand, and Håvard Rue. "Bayesian Model Averaging with the Integrated Nested Laplace Approximation." Econometrics 8, no. 2 (June 1, 2020): 23. http://dx.doi.org/10.3390/econometrics8020023.

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The integrated nested Laplace approximation (INLA) for Bayesian inference is an efficient approach to estimate the posterior marginal distributions of the parameters and latent effects of Bayesian hierarchical models that can be expressed as latent Gaussian Markov random fields (GMRF). The representation as a GMRF allows the associated software R-INLA to estimate the posterior marginals in a fraction of the time as typical Markov chain Monte Carlo algorithms. INLA can be extended by means of Bayesian model averaging (BMA) to increase the number of models that it can fit to conditional latent GMRF. In this paper, we review the use of BMA with INLA and propose a new example on spatial econometrics models.

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Brix, Gunnar, Stefan Zwick, Fabian Kiessling, and Jürgen Griebel. "Pharmacokinetic analysis of tissue microcirculation using nested models: Multimodel inference and parameter identifiability." Medical Physics 36, no. 7 (June 9, 2009): 2923–33. http://dx.doi.org/10.1118/1.3147145.

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Scheffler, Guillermo, Juan Ruiz, and Manuel Pulido. "Inference of stochastic parametrizations for model error treatment using nested ensemble Kalman filters." Quarterly Journal of the Royal Meteorological Society 145, no. 722 (May 2, 2019): 2028–45. http://dx.doi.org/10.1002/qj.3542.

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Bartolucci, Francesco, and Monia Lupparelli. "Pairwise Likelihood Inference for Nested Hidden Markov Chain Models for Multilevel Longitudinal Data." Journal of the American Statistical Association 111, no. 513 (January 2, 2016): 216–28. http://dx.doi.org/10.1080/01621459.2014.998935.

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Rue, Håvard, Sara Martino, and Nicolas Chopin. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 71, no. 2 (April 2009): 319–92. http://dx.doi.org/10.1111/j.1467-9868.2008.00700.x.

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Carbone, I., and L. M. Kohn. "A microbial population-species interface: nested cladistic and coalescent inference with multilocus data." Molecular Ecology 10, no. 4 (April 2001): 947–64. http://dx.doi.org/10.1046/j.1365-294x.2001.01244.x.

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Mandel, David R. "Nested sets theory, full stop: Explaining performance on Bayesian inference tasks without dual-systems assumptions." Behavioral and Brain Sciences 30, no. 3 (June 2007): 275–76. http://dx.doi.org/10.1017/s0140525x07001835.

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AbstractConsistent with Barbey & Sloman (B&S), it is proposed that performance on Bayesian inference tasks is well explained by nested sets theory (NST). However, contrary to those authors' view, it is proposed that NST does better by dispelling with dual-systems assumptions. This article examines why, and sketches out a series of NST's core principles, which were not previously defined.

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Rohlfing, Ingo. "What You See and What You Get." Comparative Political Studies 41, no. 11 (October 24, 2007): 1492–514. http://dx.doi.org/10.1177/0010414007308019.

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In a recent contribution to this journal, Munck and Snyder found that many studies suffer from a deficient application of qualitative and quantitative methods. They argue that the combination of small- n and large- n analysis represents a viable method for promoting the production of knowledge. Recently, Evan Lieberman proposed nested analysis as a rigorous approach for comparative research that builds on the complementary strengths of quantitative and qualitative analysis. In this paper, the author examines the methodological potential of nested inference to advance comparative political analysis, arguing that the specific methodological problems of nested designs have not been fully appreciated. It is shown that, under certain circumstances, nothing is gained from a nested analysis. On the contrary, one might lose more than one gains compared to single-method designs. The author suggests specific methodological principles that take these problems into account to make nested analysis fruitful for comparative studies.

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Leon-Novelo, L. G., X. Zhou, B. Nebiyou Bekele, and P. Müller. "Assessing Toxicities in a Clinical Trial: Bayesian Inference for Ordinal Data Nested within Categories." Biometrics 66, no. 3 (November 23, 2009): 966–74. http://dx.doi.org/10.1111/j.1541-0420.2009.01359.x.

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Xu, Yuhang, Yehua Li, and Dan Nettleton. "Nested Hierarchical Functional Data Modeling and Inference for the Analysis of Functional Plant Phenotypes." Journal of the American Statistical Association 113, no. 522 (April 3, 2018): 593–606. http://dx.doi.org/10.1080/01621459.2017.1366907.

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Aitken, Stuart, and Ozgur E. Akman. "Nested sampling for parameter inference in systems biology: application to an exemplar circadian model." BMC Systems Biology 7, no. 1 (2013): 72. http://dx.doi.org/10.1186/1752-0509-7-72.

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Pérez-Vieites, Sara, and Joaquín Míguez. "Nested Gaussian filters for recursive Bayesian inference and nonlinear tracking in state space models." Signal Processing 189 (December 2021): 108295. http://dx.doi.org/10.1016/j.sigpro.2021.108295.

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Fang, Sheng-En, Jia-li Tan, and Xiao-Hua Zhang. "Safety evaluation of truss structures using nested discrete Bayesian networks." Structural Health Monitoring 19, no. 6 (March 4, 2020): 1924–36. http://dx.doi.org/10.1177/1475921720907888.

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Truss structures have been widely adopted for civil structures such as long-span buildings and bridges. An actual truss system is usually statically indeterminate having numerous members and high redundancy. It is practically difficult to evaluate the truss safety through traditional reliability-based approaches in view of complex failure modes and uncertainties. Moreover, monitoring data are generally insufficient in reality due to limited sensors under cost consideration. Therefore, a nested discrete Bayesian network has been developed for safety evaluation of truss structures. A concept of member risk coefficient is first proposed based on the mechanical relationship between load effects and member resistance. According to the coefficients of all members, member risk sequences are found as the basis for establishing the topology of a member-level Bayesian network. Each network node represents a truss member and a nodal variable having three states: elasticity, plasticity, and failure. Two relevant member nodes are connected by a directed edge whose causality strength is expressed by a conditional probability table. Meanwhile, a system-level network topology is established to reflect the effects of member states on the truss system. The system is assigned with a node having two states: safety and failure. The directed edge of each member node directly points to the system node. Then, the two networks are combined to form a nested network topology. By this means, direct topology learning is avoided in order to find rational and concise topologies satisfying the mechanical characteristics of civil structures. After that, the conditional probability tables for the nested network are obtained through parameter learning on complete numerical observation data. The data acquirement procedure takes into account uncertainties by defining the randomness of cross-sectional areas and external loads. With the conditional probability tables, the nested network is ready for use. When new evidence from limited monitored members is input into the nested network, the state probabilities of the other members, as well as the system, are simultaneously updated using exact inference algorithms. The inference ability using insufficient information well accords with the demand of engineering practice. Finally, the proposed method has been successfully verified against both numerical and experimental truss structures. It was found that the network estimations could be further confirmed with more evidence.

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Harbers, Imke, and Matthew C. Ingram. "Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data." Political Analysis 25, no. 3 (May 15, 2017): 289–307. http://dx.doi.org/10.1017/pan.2017.4.

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Mixed-methods designs, especially those where cases selected for small-N analysis (SNA) are nested within a large-N analysis (LNA), have become increasingly popular. Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial dependence, and dependence becomes a threat to inference, rather than an issue for empirical or theoretical investigation. This is unfortunate, since research in political science has recently drawn attention to diffusion and interconnectedness more broadly. In this paper we develop a framework for mixed-methods research with spatially dependent data—a framework we label “geo-nested analysis”—where insights gleaned at each step of the research process set the agenda for the next phase and where case selection for SNA is based on diagnostics of a spatial-econometric analysis. We illustrate our framework using data from a seminal study of homicides in the United States.

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Shibuya, Takashi, and Eduard Hovy. "Nested Named Entity Recognition via Second-best Sequence Learning and Decoding." Transactions of the Association for Computational Linguistics 8 (September 2020): 605–20. http://dx.doi.org/10.1162/tacl_a_00334.

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When an entity name contains other names within it, the identification of all combinations of names can become difficult and expensive. We propose a new method to recognize not only outermost named entities but also inner nested ones. We design an objective function for training a neural model that treats the tag sequence for nested entities as the second best path within the span of their parent entity. In addition, we provide the decoding method for inference that extracts entities iteratively from outermost ones to inner ones in an outside-to-inside way. Our method has no additional hyperparameters to the conditional random field based model widely used for flat named entity recognition tasks. Experiments demonstrate that our method performs better than or at least as well as existing methods capable of handling nested entities, achieving F1-scores of 85.82%, 84.34%, and 77.36% on ACE-2004, ACE-2005, and GENIA datasets, respectively.

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Zhang, Xinyu, and Chu-An Liu. "INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS." Econometric Theory 35, no. 4 (September 4, 2018): 816–41. http://dx.doi.org/10.1017/s0266466618000269.

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This article considers the problem of inference for nested least squares averaging estimators. We study the asymptotic behavior of the Mallows model averaging estimator (MMA; Hansen, 2007) and the jackknife model averaging estimator (JMA; Hansen and Racine, 2012) under the standard asymptotics with fixed parameters setup. We find that both MMA and JMA estimators asymptotically assign zero weight to the under-fitted models, and MMA and JMA weights of just-fitted and over-fitted models are asymptotically random. Building on the asymptotic behavior of model weights, we derive the asymptotic distributions of MMA and JMA estimators and propose a simulation-based confidence interval for the least squares averaging estimator. Monte Carlo simulations show that the coverage probabilities of proposed confidence intervals achieve the nominal level.

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Jazić, Ina, Stephanie Lee, and Sebastien Haneuse. "Estimation and inference for semi-competing risks based on data from a nested case-control study." Statistical Methods in Medical Research 29, no. 11 (June 17, 2020): 3326–39. http://dx.doi.org/10.1177/0962280220926219.

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In semi-competing risks, the occurrence of some non-terminal event is subject to a terminal event, usually death. While existing methods for semi-competing risks data analysis assume complete information on all relevant covariates, data on at least one covariate are often not readily available in practice. In this setting, for standard univariate time-to-event analyses, researchers may choose from several strategies for sub-sampling patients on whom to collect complete data, including the nested case-control study design. Here, we consider a semi-competing risks analysis through the reuse of data from an existing nested case-control study for which risk sets were formed based on either the non-terminal or the terminal event. Additionally, we introduce the supplemented nested case-control design in which detailed data are collected on additional events of the other type. We propose estimation with respect to a frailty illness-death model through maximum weighted likelihood, specifying the baseline hazard functions either parametrically or semi-parametrically via B-splines. Two standard error estimators are proposed: (i) a computationally simple sandwich estimator and (ii) an estimator based on a perturbation resampling procedure. We derive the asymptotic properties of the proposed methods and evaluate their small-sample properties via simulation. The designs/methods are illustrated with an investigation of risk factors for acute graft-versus-host disease among N = 8838 patients undergoing hematopoietic stem cell transplantation, for which death is a significant competing risk.

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Moss, Adam. "Accelerated Bayesian inference using deep learning." Monthly Notices of the Royal Astronomical Society 496, no. 1 (May 28, 2020): 328–38. http://dx.doi.org/10.1093/mnras/staa1469.

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ABSTRACT We present a novel Bayesian inference tool that uses a neural network (NN) to parametrize efficient Markov Chain Monte Carlo (MCMC) proposals. The target distribution is first transformed into a diagonal, unit variance Gaussian by a series of non-linear, invertible, and non-volume preserving flows. NNs are extremely expressive, and can transform complex targets to a simple latent representation. Efficient proposals can then be made in this space, and we demonstrate a high degree of mixing on several challenging distributions. Parameter space can naturally be split into a block diagonal speed hierarchy, allowing for fast exploration of subspaces where it is inexpensive to evaluate the likelihood. Using this method, we develop a nested MCMC sampler to perform Bayesian inference and model comparison, finding excellent performance on highly curved and multimodal analytic likelihoods. We also test it on Planck 2015 data, showing accurate parameter constraints, and calculate the evidence for simple one-parameter extensions to the standard cosmological model in ∼20D parameter space. Our method has wide applicability to a range of problems in astronomy and cosmology and is available for download from https://github.com/adammoss/nnest.

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Aylott, Benjamin, John Veitch, and Alberto Vecchio. "Bayesian inference on the Numerical INJection Analysis (NINJA) data set using a nested sampling algorithm." Classical and Quantum Gravity 26, no. 11 (May 19, 2009): 114011. http://dx.doi.org/10.1088/0264-9381/26/11/114011.

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Elsheikh, Ahmed H., Ibrahim Hoteit, and Mary F. Wheeler. "Efficient Bayesian inference of subsurface flow models using nested sampling and sparse polynomial chaos surrogates." Computer Methods in Applied Mechanics and Engineering 269 (February 2014): 515–37. http://dx.doi.org/10.1016/j.cma.2013.11.001.

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Schultz, Michael. "The Problem of Underdetermination in Model Selection." Sociological Methodology 48, no. 1 (July 13, 2018): 52–87. http://dx.doi.org/10.1177/0081175018786762.

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Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference. To address this problem, the author proposes a new paradigm for inference-oriented model selection that evaluates models on the basis of a trade-off between model fit and model plausibility. By comparing the fits of sequentially nested models, it is possible to derive an empirical lower bound for the subjective plausibility of assumptions. To demonstrate the effectiveness of this approach, the method is applied to models of the relationship between cultural tastes and network composition.

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Naimi, Ashley I., David B. Richardson, and Stephen R. Cole. "Causal Inference in Occupational Epidemiology: Accounting for the Healthy Worker Effect by Using Structural Nested Models." American Journal of Epidemiology 178, no. 12 (September 27, 2013): 1681–86. http://dx.doi.org/10.1093/aje/kwt215.

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Journal articles on the topic 'Nested inference'

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