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Relevant bibliographies by topics / Nested inference

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Academic literature on the topic 'Nested inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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Journal articles on the topic "Nested inference"

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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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Dissertations / Theses on the topic "Nested inference"

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Ventura, Valerie. "Likelihood inference by Monte Carlo methods and efficient nested bootstrapping." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362032.

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Kim, Junyeop. "Causal inference in multilevel settings estimating and using propensity scores when treatment is implemented in nested settings /." Diss., Restricted to subscribing institutions, 2006. http://proquest.umi.com/pqdweb?did=1280132651&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Marklund, Emil. "Bayesian inference in aggregated hidden Markov models." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-243090.

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Single molecule experiments study the kinetics of molecular biological systems. Many such studies generate data that can be described by aggregated hidden Markov models, whereby there is a need of doing inference on such data and models. In this study, model selection in aggregated Hidden Markov models was performed with a criterion of maximum Bayesian evidence. Variational Bayes inference was seen to underestimate the evidence for aggregated model fits. Estimation of the evidence integral by brute force Monte Carlo integration theoretically always converges to the correct value, but it converges in far from tractable time. Nested sampling is a promising method for solving this problem by doing faster Monte Carlo integration, but it was here seen to have difficulties generating uncorrelated samples.

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Seaman, Iris Rubi. "Probabilistic Programming for Theory of Mind for Autonomous Decision Making." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/6826.

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As autonomous agents (such as unmanned aerial vehicles, or UAVs) become more ubiquitous, they are being used for increasingly complex tasks. Eventually, they will have to reason about the mental state of other agents, including those agents' beliefs, desires and goals – so-called Theory of Mind – and make decisions based on that reasoning. We describe increasingly complex theory of mind models of a UAV pursuing an intruder, and show that (1) there is a natural Bayesian formulation to reasoning about the uncertainty inherent in our estimate of another agent's mental state, and that (2) probabilistic programming is a natural way to describe models that involve one agent reasoning about another agent, where the target agent uses complex primitives such as path planners and saliency maps to make decisions. We propose a nested self-normalized importance sampling inference algorithm for probabilistic programs, and show that it can be used with planning-as-inference to simultaneously reason about other agents' plans and craft counter plans. We demonstrate that more complex models lead to improved performance, and that nested modeling manifests a wide variety of rational agent behavior.

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Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.

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This thesis is concerned with methods for Bayesian inference and their applications in astrophysics. We principally discuss two related themes: advances in nested sampling (Chapters 3 to 5), and Bayesian sparse reconstruction of signals from noisy data (Chapters 6 and 7). Nested sampling is a popular method for Bayesian computation which is widely used in astrophysics. Following the introduction and background material in Chapters 1 and 2, Chapter 3 analyses the sampling errors in nested sampling parameter estimation and presents a method for estimating them numerically for a single nested sampling calculation. Chapter 4 introduces diagnostic tests for detecting when software has not performed the nested sampling algorithm accurately, for example due to missing a mode in a multimodal posterior. The uncertainty estimates and diagnostics in Chapters 3 and 4 are implemented in the $\texttt{nestcheck}$ software package, and both chapters describe an astronomical application of the techniques introduced. Chapter 5 describes dynamic nested sampling: a generalisation of the nested sampling algorithm which can produce large improvements in computational efficiency compared to standard nested sampling. We have implemented dynamic nested sampling in the $\texttt{dyPolyChord}$ and $\texttt{perfectns}$ software packages. Chapter 6 presents a principled Bayesian framework for signal reconstruction, in which the signal is modelled by basis functions whose number (and form, if required) is determined by the data themselves. This approach is based on a Bayesian interpretation of conventional sparse reconstruction and regularisation techniques, in which sparsity is imposed through priors via Bayesian model selection. We demonstrate our method for noisy 1- and 2-dimensional signals, including examples of processing astronomical images. The numerical implementation uses dynamic nested sampling, and uncertainties are calculated using the methods introduced in Chapters 3 and 4. Chapter 7 applies our Bayesian sparse reconstruction framework to artificial neural networks, where it allows the optimum network architecture to be determined by treating the number of nodes and hidden layers as parameters. We conclude by suggesting possible areas of future research in Chapter 8.

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Hee, Sonke. "Computational Bayesian techniques applied to cosmology." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/273346.

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This thesis presents work around 3 themes: dark energy, gravitational waves and Bayesian inference. Both dark energy and gravitational wave physics are not yet well constrained. They present interesting challenges for Bayesian inference, which attempts to quantify our knowledge of the universe given our astrophysical data. A dark energy equation of state reconstruction analysis finds that the data favours the vacuum dark energy equation of state $w {=} -1$ model. Deviations from vacuum dark energy are shown to favour the super-negative ‘phantom’ dark energy regime of $w {< } -1$, but at low statistical significance. The constraining power of various datasets is quantified, finding that data constraints peak around redshift $z = 0.2$ due to baryonic acoustic oscillation and supernovae data constraints, whilst cosmic microwave background radiation and Lyman-$\alpha$ forest constraints are less significant. Specific models with a conformal time symmetry in the Friedmann equation and with an additional dark energy component are tested and shown to be competitive to the vacuum dark energy model by Bayesian model selection analysis: that they are not ruled out is believed to be largely due to poor data quality for deciding between existing models. Recent detections of gravitational waves by the LIGO collaboration enable the first gravitational wave tests of general relativity. An existing test in the literature is used and sped up significantly by a novel method developed in this thesis. The test computes posterior odds ratios, and the new method is shown to compute these accurately and efficiently. Compared to computing evidences, the method presented provides an approximate 100 times reduction in the number of likelihood calculations required to compute evidences at a given accuracy. Further testing may identify a significant advance in Bayesian model selection using nested sampling, as the method is completely general and straightforward to implement. We note that efficiency gains are not guaranteed and may be problem specific: further research is needed.

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Vatka, E. (Emma). "Boreal populations facing climatic and habitat changes." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526203607.

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Abstract Anthropogenic climate change and habitat loss and deterioration affect populations worldwide. Climate warming has changed phenologies of many species across trophic levels. Some predator populations now experience temporal mismatches with their prey, as timings of peak prey abundance and of the predator’s highest food demands no longer meet. The temporal mismatch hypothesis suggests that the population’s recruitment rate is related to its degree of synchrony with the food resources needed to feed offspring. However, species’ and populations’ responses to climate warming differ. Human land use alters and destroys habitats of countless species. For example, many boreal forest bird populations have declined, presumably due to intensive forestry. It has decreased the amount of dead wood, causing a threat to saproxylic species. Identification of the key characteristics of high-quality habitats is essential for conservation planning and for developing sustainable forestry. As individuals are suspected to settle in habitats that maximize their fitness, analysis of nest site selection can be used to identify the key habitats. My dissertation concerns the impacts of climate change and habitat deterioration on boreal populations. I use hole-nesting passerines as model species. By utilizing long-term data I show that breeding phenologies of Parus major and Poecile montanus, but not of Cyanistes caeruleus, have shifted earlier. Also, the timing of the food peak has advanced, improving the synchrony between P. montanus and caterpillars. In P. major and C. caeruleus, synchrony has remained good. However, the positive effect of good synchrony on breeding success seems to be conditional, arising only in certain circumstances, such as in years of high caterpillar abundance. I suggest that in boreal populations temperature affects timing of breeding mostly as a proximate factor. The availability of standing decaying trees used for nesting sites was the most important habitat characteristic determining the nest site selection of P. montanus. Remote sensing data alone was insufficient to produce reliable models, as the ecologically important small-scale factor can only be determined by direct field surveys. Omission of forest thinning in selected forest sites would ensure the continuous availability of decaying wood with positive influence on biodiversity in managed forests
Tiivistelmä Ihmisen aiheuttama habitaattien katoaminen ja huononeminen sekä ilmastonmuutos vaikuttavat populaatioihin kaikkialla maailmassa. Ilmaston lämpeneminen on muuttanut monien lajien fenologioita eri trofiatasoilla. Osalla saalistajalajien populaatioista suurin ravinnontarve ei enää ajoitu samaan aikaan saaliin runsaushuipun kanssa. Ajoituksen eriaikaisuus -hypoteesin mukaan todennäköisyys populaatioon rekrytoitumiselle riippuu synkronian asteesta saaliin kanssa. Ilmaston lämpenemisen vaikutuksissa lajeihin ja populaatioihin on kuitenkin vaihtelua. Ihmisen maankäyttö muuttaa ja tuhoaa lukuisien lajien elinympäristöjä. Esimerkiksi useiden boreaalisten metsien lintupopulaatioiden pienentymistä on selitetty intensiivisellä metsätaloudella. Lahopuun määrä metsissä on vähentynyt, mikä on uhka lahopuusta riippuvaisille lajeille. Korkealaatuisten habitaattien keskeisten piirteiden tunnistaminen on tärkeää luonnonsuojelun ja kestävän metsätalouden suunnittelulle. Koska yksilöiden oletetaan valitsevan niiden kelpoisuutta maksimoivia elinympäristöjä, pesäpaikanvalinta-analyysiä voidaan käyttää tärkeiden habitaattipiirteiden tunnistamiseen. Tarkastelen väitöskirjassani ilmastonmuutoksen ja habitaattien laadun vaikutuksia boreaalisiin populaatioihin. Mallilajeina käytän koloissa pesiviä varpuslintuja. Hyödyntämällä pitkäaikaisaineistoja osoitan, että lisääntymisen ajoittuminen on aikaistunut tali- ja hömötiaisella, mutta ei sinitiaisella. Myös ravintohuippu on aikaistunut, mikä on parantanut synkroniaa hömötiaisen ja sen pääasiallisen ravinnon eli toukkien välillä. Tali- ja sinitiaisella synkronia on pysynyt hyvänä. Hyvän synkronian myönteinen vaikutus lisääntymismenestykseen vaikuttaa kuitenkin ehdolliselta: se tulee esiin vain tietyissä olosuhteissa, kuten vuosina jolloin toukkia on runsaasti. Kevään lämpötilat näyttävät vaikuttavan pesinnän ajoittumiseen erityisesti proksimaattisena tekijänä. Pesäpaikkoina toimivien seisovien lahopuiden määrä on tärkein hömötiaisen pesäpaikanvalintaa määräävä tekijä. Kaukokartoitusaineisto yksinään ei riitä luotettavien mallien tuottamiseen, sillä ekologisesti tärkeät pienen skaalan tekijät voidaan kartoittaa vain suorin maastomittauksin. Metsien harventamatta jättäminen valituilla laikuilla turvaisi lahopuun jatkuvan saatavuuden, mikä vaikuttaisi myönteisesti talousmetsien biodiversiteettiin

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Chammah, Tarek. "Nested pessimistic transactions for both atomicity and synchronization in concurrent software." Thesis, 2011. http://hdl.handle.net/10012/6350.

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Existing atomic section interface proposals, thus far, have tended to only isolate transactions from each other. Less considered is the coordination of threads performing transactions with respect to one another. Synchronization of nested sections is typically relegated to outside of and among the top-level flattened sections. However existing models do not permit the composition of even simple synchronization constructs such as barriers. The proposed model integrates synchronization as a first-class construct in a truly nested atomic block implementation. The implementation is evaluated on quantitative benchmarks, with qualitative examples of the atomic section interface???s expressive power compared with conventional transactional memory implementations.

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Lu, Li-Tien, and 呂理添. "Statistical Inference for Functions of Variance Componentsunder Two-Way Crossed or Nested Random-Effects Models with Applications to Heritability and Reproducibility of Assay Validation." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62754389314906325911.

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博士
國立臺灣大學
農藝學研究所
97
Various different approaches for constructing confidence interval for functions of variance components proposed under cross-classification or nested random-effects models. However, these approaches are approximate and their probability coverage is either conservative or liberal. Their performances under the imbalanced situations are not fully investigated and hence remain unknown. Therefore, we apply the concept of Generalized Pivotal Quantities (GPQs) to obtain the exact confidence intervals under the two-way cross-classification with interaction random-effects model and the two-stage nested random-effects model . The exact confidence interval can be used to test the hypothesis whether the interested measure of variance components exceeds a pre-specified threshold. This hypothesis can be applied to the heritability study of animal and plant breeding and the gauge repeatability and reproducibility (R&R) study and to the reliability in validation studies during the development of instruments. A large simulation study was conducted to empirically investigate the coverage probability and expected length of the proposed exact confidence intervals, and size and power of the proposed testing procedures based on the exact confidence intervals. Numeric data from public domains illustrate the applications of the proposed methods.

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Pham, David. "Densités de copules archimédiennes hiérarchiques." Thèse, 2012. http://hdl.handle.net/1866/8529.

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Les copulas archimédiennes hiérarchiques ont récemment gagné en intérêt puisqu’elles généralisent la famille de copules archimédiennes, car elles introduisent une asymétrie partielle. Des algorithmes d’échantillonnages et des méthodes ont largement été développés pour de telles copules. Néanmoins, concernant l’estimation par maximum de vraisemblance et les tests d’adéquations, il est important d’avoir à disposition la densité de ces variables aléatoires. Ce travail remplie ce manque. Après une courte introduction aux copules et aux copules archimédiennes hiérarchiques, une équation générale sur les dérivées des noeuds et générateurs internes apparaissant dans la densité des copules archimédiennes hiérarchique. sera dérivée. Il en suit une formule tractable pour la densité des copules archimédiennes hiérarchiques. Des exemples incluant les familles archimédiennes usuelles ainsi que leur transformations sont présentés. De plus, une méthode numérique efficiente pour évaluer le logarithme des densités est présentée.
Nested Archimedean copulas recently gained interest since they generalize the well-known class of Archimedean copulas to allow for partial asymmetry. Sampling algorithms and strategies have been well investigated for nested Archimedean copulas. However, for likelihood based inference such as estimation or goodness-of-fit testing it is important to have the density. The present work fills this gap. After a short introduction on copula and nested Archimedean copulas, a general formula for the derivatives of the nodes and inner generators appearing in nested Archimedean copulas is developed. This leads to a tractable formula for the density of nested Archimedean copulas. Various examples including famous Archimedean families and transformations of such are given. Furthermore, a numerically efficient way to evaluate the log-density is presented.

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Books on the topic "Nested inference"

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Bhatti, M. Ishaq. Non-Nested Regression Models: UK ed. edition. Hauppauge, New York, USA: Nova Science Publishers Inc, 2013.

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G, Grubb Teryl, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. Evaluating Great Lakes bald eagle nesting habitat with Bayesian inference. Ft. Collins, CO: United States Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2003.

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Book chapters on the topic "Nested inference"

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Salzberg, Steven. "Nested hyper-rectangles for exemplar-based learning." In Analogical and Inductive Inference, 184–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51734-0_61.

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Bono, Viviana, Jerzy Tiuryn, and Paweł Urzyczyn. "Type Inference for Nested Self Types." In Lecture Notes in Computer Science, 99–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24849-1_7.

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Huckemann, Stephan F., and Benjamin Eltzner. "Essentials of backward nested descriptors inference." In Contributions to Statistics, 137–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55846-2_18.

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Kjærulff, Uffe. "Inference in Bayesian Networks Using Nested Junction Trees." In Learning in Graphical Models, 51–74. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5014-9_3.

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Eltzner, Benjamin, and Stephan Huckemann. "Applying Backward Nested Subspace Inference to Tori and Polyspheres." In Lecture Notes in Computer Science, 587–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68445-1_68.

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Goré, Rajeev, Linda Postniece, and Alwen Tiu. "Taming Displayed Tense Logics Using Nested Sequents with Deep Inference." In Lecture Notes in Computer Science, 189–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02716-1_15.

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Robins, James M. "Marginal Structural Models versus Structural nested Models as Tools for Causal inference." In Statistical Models in Epidemiology, the Environment, and Clinical Trials, 95–133. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1284-3_2.

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Helian, Shanjun, Babette A. Brumback, Matthew C. Freeman, and Richard Rheingans. "Structural Nested Models for Cluster-Randomized Trials." In Statistical Causal Inferences and Their Applications in Public Health Research, 169–86. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41259-7_9.

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"NESTED INFERENCE." In Dictating Development, 84–94. University of Pittsburgh Press, 2006. http://dx.doi.org/10.2307/j.ctv10tq46z.9.

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Gómez-Rubio, Virgilio. "The Integrated Nested Laplace Approximation." In Bayesian Inference with INLA, 13–38. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781315175584-2.

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Conference papers on the topic "Nested inference"

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Skilling, John. "Nested Sampling." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835238.

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Skilling, John, Paul M. Goggans, and Chun-Yong Chan. "Nested Sampling’s Convergence." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2009. http://dx.doi.org/10.1063/1.3275625.

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Henderson, R. Wesley, and Paul M. Goggans. "Parallelized nested sampling." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 33rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2013). AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4903717.

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Habeck, Michael. "Nested sampling with demons." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING (MAXENT 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4905971.

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Feroz, Farhan, and John Skilling. "Exploring multi-modal distributions with nested sampling." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 32nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2013. http://dx.doi.org/10.1063/1.4819989.

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Betancourt, Michael, Ali Mohammad-Djafari, Jean-François Bercher, and Pierre Bessiére. "Nested Sampling with Constrained Hamiltonian Monte Carlo." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 30th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2011. http://dx.doi.org/10.1063/1.3573613.

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Skilling, John. "Bayesian computation in big spaces-nested sampling and Galilean Monte Carlo." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 31st International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2012. http://dx.doi.org/10.1063/1.3703630.

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Stokes, Barrie, Frank Tuyl, and Irene Hudson. "New prior sampling methods for nested sampling - Development and testing." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2016). Author(s), 2017. http://dx.doi.org/10.1063/1.4985378.

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Zhang, Yipeng, Bo Du, Lefei Zhang, Rongchun Li, and Yong Dou. "Accelerated Inference Framework of Sparse Neural Network Based on Nested Bitmask Structure." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/605.

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Abstract:

In order to satisfy the ever-growing demand for high-performance processors for neural networks, the state-of-the-art processing units tend to use application-oriented circuits to replace Processing Engine (PE) on the GPU under circumstances where low-power solutions are required. The application-oriented PE is fully optimized in terms of the circuit architecture and eliminates incorrect data dependency and instructional redundancy. In this paper, we propose a novel encoding approach on a sparse neural network after pruning. We partition the weight matrix into numerous blocks and use a low-rank binary map to represent the validation of these blocks. Furthermore, the elements in each nonzero block are also encoded into two submatrices: one is the binary stream discriminating the zero/nonzero position, while the other is the pure nonzero elements stored in the FIFO. In the experimental part, we implement a well pre-trained sparse neural network on the Xilinx FPGA VC707. Experimental results show that our algorithm outperforms the other benchmarks. Our approach has successfully optimized the throughput and the energy efficiency to deal with a single frame. Accordingly, we contend that Nested Bitmask Neural Network (NBNN), is an efficient neural network structure with only minor accuracy loss on the SoC system.

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Perez-Vieites, Sara, and Joaquin Miguez. "Kalman-based nested hybrid filters for recursive inference in state-space models." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287359.

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