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Academic literature on the topic 'Multi-model inference'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Multi-model inference"

Millington, James D. A., and George L. W. Perry. "Multi-Model Inference in Biogeography." Geography Compass 5, no. 7 (July 2011): 448–63. http://dx.doi.org/10.1111/j.1749-8198.2011.00433.x.

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WANG, Hui-Zhen, and Jing-Bo ZHU. "Optimizations of Multi-Aspect Rating Inference Model." Journal of Software 24, no. 7 (January 16, 2014): 1545–56. http://dx.doi.org/10.3724/sp.j.1001.2013.04278.

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Theorell, Axel, and Katharina Nöh. "Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis." Bioinformatics 36, no. 1 (June 19, 2019): 232–40. http://dx.doi.org/10.1093/bioinformatics/btz500.

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Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.

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Mao, W., and J. Gratch. "Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions." Journal of Artificial Intelligence Research 44 (May 30, 2012): 223–73. http://dx.doi.org/10.1613/jair.3526.

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Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multi-agent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multi-agent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agents causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality.

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Katsanevakis, Stelios. "Modelling fish growth: Model selection, multi-model inference and model selection uncertainty." Fisheries Research 81, no. 2-3 (November 2006): 229–35. http://dx.doi.org/10.1016/j.fishres.2006.07.002.

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Lee, Bong-Keun, Jae-Du Chung, and Keun-Ho Ryu. "Multi-Agent Reinforcement Learning Model based on Fuzzy Inference." Journal of the Korea Contents Association 9, no. 10 (October 28, 2009): 51–58. http://dx.doi.org/10.5392/jkca.2009.9.10.051.

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Yao, Yuan, Hanghang Tong, Xifeng Yan, Feng Xu, and Jian Lu. "Multi-Aspect + Transitivity + Bias: An Integral Trust Inference Model." IEEE Transactions on Knowledge and Data Engineering 26, no. 7 (July 2014): 1706–19. http://dx.doi.org/10.1109/tkde.2013.147.

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Stumpf, Michael P. H., and Thomas Thorne. "Multi-model inference of network properties from incomplete data." Journal of Integrative Bioinformatics 3, no. 2 (December 1, 2006): 123–36. http://dx.doi.org/10.1515/jib-2006-32.

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Summary It has previously been shown that subnets differ from global networks from which they are sampled for all but a very limited number of theoretical network models. These differences are of qualitative as well as quantitative nature, and the properties of subnets may be very different from the corresponding properties in the true, unobserved network. Here we propose a novel approach which allows us to infer aspects of the true network from incomplete network data in a multi-model inference framework. We develop the basic theoretical framework, including procedures for assessing confidence intervals of our estimates and evaluate the performance of this approach in simulation studies and against subnets drawn from the presently available PIN network data in Saccaromyces cerevisiae. We then illustrate the potential power of this new approach by estimating the number of interactions that will be detectable with present experimental approaches in sfour eukaryotic species, inlcuding humans. Encouragingly, where independent datasets are available we obtain consistent estimates from different partial protein interaction networks. We conclude with a discussion of the scope of this approaches and areas for further research

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Mu, He-Qing, Han-Teng Liu, and Ji-Hui Shen. "Copula-Based Uncertainty Quantification (Copula-UQ) for Multi-Sensor Data in Structural Health Monitoring." Sensors 20, no. 19 (October 6, 2020): 5692. http://dx.doi.org/10.3390/s20195692.

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The problem of uncertainty quantification (UQ) for multi-sensor data is one of the main concerns in structural health monitoring (SHM). One important task is multivariate joint probability density function (PDF) modelling. Copula-based statistical inference has attracted significant attention due to the fact that it decouples inferences on the univariate marginal PDF of each random variable and the statistical dependence structure (called copula) among the random variables. This paper proposes the Copula-UQ, composing multivariate joint PDF modelling, inference on model class selection and parameter identification, and probabilistic prediction using incomplete information, for multi-sensor data measured from a SHM system. Multivariate joint PDF is modeled based on the univariate marginal PDFs and the copula. Inference is made by combing the idea of the inference functions for margins and the maximum likelihood estimate. Prediction on the PDF of the target variable, using the complete (from normal sensors) or incomplete information (due to missing data caused by sensor fault issue) of the predictor variable, are made based on the multivariate joint PDF. One example using simulated data and one example using temperature data of a multi-sensor of a monitored bridge are presented to illustrate the capability of the Copula-UQ in joint PDF modelling and target variable prediction.

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Liu, Jingyu, Qiong Wang, Dunbo Zhang, and Li Shen. "Super-Resolution Model Quantized in Multi-Precision." Electronics 10, no. 17 (September 6, 2021): 2176. http://dx.doi.org/10.3390/electronics10172176.

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Deep learning has achieved outstanding results in various tasks in machine learning under the background of rapid increase in equipment’s computing capacity. However, while achieving higher performance and effects, model size is larger, training and inference time longer, the memory and storage occupancy increasing, the computing efficiency shrinking, and the energy consumption augmenting. Consequently, it’s difficult to let these models run on edge devices such as micro and mobile devices. Model compression technology is gradually emerging and researched, for instance, model quantization. Quantization aware training can take more accuracy loss resulting from data mapping in model training into account, which clamps and approximates the data when updating parameters, and introduces quantization errors into the model loss function. In quantization, we found that some stages of the two super-resolution model networks, SRGAN and ESRGAN, showed sensitivity to quantization, which greatly reduced the performance. Therefore, we use higher-bits integer quantization for the sensitive stage, and train the model together in quantization aware training. Although model size was sacrificed a little, the accuracy approaching the original model was achieved. The ESRGAN model was still reduced by nearly 67.14% and SRGAN model was reduced by nearly 68.48%, and the inference time was reduced by nearly 30.48% and 39.85% respectively. What’s more, the PI values of SRGAN and ESRGAN are 2.1049 and 2.2075 respectively.

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Dissertations / Theses on the topic "Multi-model inference"

Çetin, Özgür. "Multi-rate modeling, model inference, and estimation for statistical classifiers /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/5849.

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Lessios, Nicolas. "Using electroretinograms and multi-model inference to identify spectral classes of photoreceptors and relative opsin expression levels." PEERJ INC, 2017. http://hdl.handle.net/10150/625519.

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Understanding how individual photoreceptor cells factor in the spectral sensitivity of a visual system is essential to explain how they contribute to the visual ecology of the animal in question. Existing methods that model the absorption of visual pigments use templates which correspond closely to data from thin cross-sections of photoreceptor cells. However, few modeling approaches use a single framework to incorporate physical parameters of real photoreceptors, which can be fused, and can form vertical tiers. Akaike’s information criterion (AIC c ) was used here to select absorptance models of multiple classes of photoreceptor cells that maximize information, given visual system spectral sensitivity data obtained using extracellular electroretinograms and structural parameters obtained by histological methods. This framework was first used to select among alternative hypotheses of photoreceptor number. It identified spectral classes from a range of dark-adapted visual systems which have between one and four spectral photoreceptor classes. These were the velvet worm, Principapillatus hitoyensis , the branchiopod water flea, Daphnia magna , normal humans, and humans with enhanced S-cone syndrome, a condition in which S-cone frequency is increased due to mutations in a transcription factor that controls photoreceptor expression. Data from the Asian swallowtail, Papilio xuthus , which has at least five main spectral photoreceptor classes in its compound eyes, were included to illustrate potential effects of model over-simplification on multi-model inference. The multi-model framework was then used with parameters of spectral photoreceptor classes and the structural photoreceptor array kept constant. The goal was to map relative opsin expression to visual pigment concentration. It identified relative opsin expression differences for two populations of the bluefin killifish, Lucania goodei . The modeling approach presented here will be useful in selecting the most likely alternative hypotheses of opsin-based spectral photoreceptor classes, using relative opsin expression and extracellular electroretinography.

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Jiang, Huijing. "Statistical computation and inference for functional data analysis." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37087.

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My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part of my thesis focuses on developing modeling and inference procedure for functional data under spatial dependence. The methodology introduced in this part is motivated by a research study on inequities in accessibility to financial services. The first research problem in this part is concerned with a novel model-based method for clustering random time functions which are spatially interdependent. A cluster consists of time functions which are similar in shape. The time functions are decomposed into spatial global and time-dependent cluster effects using a semi-parametric model. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across curves from nearby locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small number of time points, high noise level and varying dependence structures. Over all simulation settings, the spatial-functional clustering method outperforms existing model-based clustering methods. In the case study presented in this project, we focus on estimates and classifies service accessibility patterns varying over a large geographic area (California and Georgia) and over a period of 15 years. The focus of this study is on financial services but it generally applies to any other service operation. The second research project of this part studies an association analysis of space-time varying processes, which is rigorous, computational feasible and implementable with standard software. We introduce general measures to model different aspects of the temporal and spatial association between processes varying in space and time. Using a nonparametric spatiotemporal model, we show that the proposed association estimators are asymptotically unbiased and consistent. We complement the point association estimates with simultaneous confidence bands to assess the uncertainty in the point estimates. In a simulation study, we evaluate the accuracy of the association estimates with respect to the sample size as well as the coverage of the confidence bands. In the case study in this project, we investigate the association between service accessibility and income level. The primary objective of this association analysis is to assess whether there are significant changes in the income-driven equity of financial service accessibility over time and to identify potential under-served markets. The second part of the thesis discusses novel statistical methodology for analyzing multilevel functional data including a clustering method based on a functional ANOVA model and a spatio-temporal model for functional data with a nested hierarchical structure. In this part, I introduce and compare a series of clustering approaches for multilevel functional data. For brevity, I present the clustering methods for two-level data: multiple samples of random functions, each sample corresponding to a case and each random function within a sample/case corresponding to a measurement type. A cluster consists of cases which have similar within-case means (level-1 clustering) or similar between-case means (level-2 clustering). Our primary focus is to evaluate a model-based clustering to more straightforward hard clustering methods. The clustering model is based on a multilevel functional principal component analysis. In a simulation study, we assess the estimation accuracy of our clustering algorithm under a series of settings: small vs. moderate number of time points, high noise level and small number of measurement types. We demonstrate the applicability of the clustering analysis to a real data set consisting of time-varying sales for multiple products sold by a large retailer in the U.S. My ongoing research work in multilevel functional data analysis is developing a statistical model for estimating temporal and spatial associations of a series of time-varying variables with an intrinsic nested hierarchical structure. This work has a great potential in many real applications where the data are areal data collected from different data sources and over geographic regions of different spatial resolution.

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Hamadeh, Lina. "Periodically integrated models : estimation, simulation, inference and data analysis." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/periodically-integrated-models-estimation-simulation-inference-and-data-analysis(f7b345e9-bad7-424a-9746-bfe771d7ba8c).html.

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Periodically correlated time series generally exist in several fields including hydrology, climatology, economics and finance, and are commonly modelled using periodic autoregressive (PAR) model. For a time series with stochastic periodic trend, for which a unit root is expected, a periodically integrated autoregressive PIAR model with periodic and/or seasonal unit root has been shown to be a satisfactory model. The existing theory used the multivariate methodology to study PIAR models. However, this theory is convoluted, majority of it only developed for quarterly time series and its generalisation to time series with larger number of periods is quite cumbersome. This thesis studies the existing theory and highlights its restrictions and flaws. It provides a coherent presentation of the steps for analysing PAR and PIAR models for different number of periods. It presents the different unit roots representations and compares the performance of different unit root tests available in literature. The restrictions of existing studies gave us the impetus to develop a unified theory that gives a clear understanding of the integration and unit roots in the periodic models. This theory is based on the spectral information of the multi-companion matrix of the periodic models. It is more general than the existing theory, since it can be applied to any number of periods whereas the existing methods are developed for quarterly time series. Using the multi-companion method, we specify and estimate the periodic models without the need to extract complicated restrictions on the model parameters corresponding to the unit roots, as required by NLS method. The multi-companion estimation method performed well and its performance is equivalent to the NLS estimation method that has been used in the literature. Analysing integrated multivariate models is a problematic issue in time series. The multi-companion theory provides a more general approach than the error correction method that is commonly used to analyse such time series. A modified state state representation for the seasonal periodically integrated autoregressive (SPIAR) model with periodic and seasonal unit roots is presented. Also an alternative state space representations from which the state space representations of PAR, PIAR and the seasonal periodic autoregressive (SPAR) models can be directly obtained is proposed. The seasons of the parameters in these representations have been clearly specified, which guarantees correct estimated parameters. Kalman filter have been used to estimate the parameters of these models and better estimation results are obtained when the initial values were estimated rather than when they were given.

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Winkler, Anderson M. "Widening the applicability of permutation inference." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c.

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This thesis is divided into three main parts. In the first, we discuss that, although permutation tests can provide exact control of false positives under the reasonable assumption of exchangeability, there are common examples in which global exchangeability does not hold, such as in experiments with repeated measurements or tests in which subjects are related to each other. To allow permutation inference in such cases, we propose an extension of the well known concept of exchangeability blocks, allowing these to be nested in a hierarchical, multi-level definition. This definition allows permutations that retain the original joint distribution unaltered, thus preserving exchangeability. The null hypothesis is tested using only a subset of all otherwise possible permutations. We do not need to explicitly model the degree of dependence between observations; rather the use of such permutation scheme leaves any dependence intact. The strategy is compatible with heteroscedasticity and can be used with permutations, sign flippings, or both combined. In the second part, we exploit properties of test statistics to obtain accelerations irrespective of generic software or hardware improvements. We compare six different approaches using synthetic and real data, assessing the methods in terms of their error rates, power, agreement with a reference result, and the risk of taking a different decision regarding the rejection of the null hypotheses (known as the resampling risk). In the third part, we investigate and compare the different methods for assessment of cortical volume and area from magnetic resonance images using surface-based methods. Using data from young adults born with very low birth weight and coetaneous controls, we show that instead of volume, the permutation-based non-parametric combination (NPC) of thickness and area is a more sensitive option for studying joint effects on these two quantities, giving equal weight to variation in both, and allowing a better characterisation of biological processes that can affect brain morphology.

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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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Reynolds, Toby J. "Bayesian modelling of integrated data and its application to seabird populations." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/1635.

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Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework. This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period. Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.

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(7582487), Ching-Wei Cheng. "Enhancing Multi-model Inference with Natural Selection." Thesis, 2019.

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Multi-model inference covers a wide range of modern statistical applications such as variable selection, model confidence set, model averaging and variable importance.

The performance of multi-model inference depends on the availability of candidate models, whose quality has been rarely studied in literature. In this dissertation, we study genetic algorithm (GA) in order to obtain high-quality candidate models. Inspired by the process of natural selection, GA performs genetic operations such as selection, crossover and mutation iteratively to update a collection of potential solutions (models) until convergence. The convergence properties are studied based on the Markov chain theory and used to design an adaptive termination criterion that vastly reduces the computational cost. In addition, a new schema theory is established to characterize how the current model set is improved through evolutionary process. Extensive numerical experiments are carried out to verify our theory and demonstrate the empirical power of GA, and new findings are obtained for two real data examples.

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Lambert, Valere Regis Westbrooke. "Multi-Model Inference Ranking and Applications to Physics at the Large Hadron Collider." Thesis, 2014. https://thesis.library.caltech.edu/8520/1/Thesis.pdf.

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In the measurement of the Higgs Boson decaying into two photons the parametrization of an appropriate background model is essential for fitting the Higgs signal mass peak over a continuous background. This diphoton background modeling is crucial in the statistical process of calculating exclusion limits and the significance of observations in comparison to a background-only hypothesis. It is therefore ideal to obtain knowledge of the physical shape for the background mass distribution as the use of an improper function can lead to biases in the observed limits. Using an Information-Theoretic (I-T) approach for valid inference we apply Akaike Information Criterion (AIC) as a measure of the separation for a fitting model from the data. We then implement a multi-model inference ranking method to build a fit-model that closest represents the Standard Model background in 2013 diphoton data recorded by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC). Potential applications and extensions of this model-selection technique are discussed with reference to CMS detector performance measurements as well as in potential physics analyses at future detectors.

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(9154928), Aritra Mitra. "New Approaches to Distributed State Estimation, Inference and Learning with Extensions to Byzantine-Resilience." Thesis, 2020.

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In this thesis, we focus on the problem of estimating an unknown quantity of interest, when the information required to do so is dispersed over a network of agents. In particular, each agent in the network receives sequential observations generated by the unknown quantity, and the collective goal of the network is to eventually learn this quantity by means of appropriately crafted information diffusion rules. The abstraction described above can be used to model a variety of problems ranging from environmental monitoring of a dynamical process using autonomous robot teams, to statistical inference using a network of processors, to social learning in groups of individuals. The limited information content of each agent, coupled with dynamically changing networks, the possibility of adversarial attacks, and constraints imposed by the communication channels, introduce various unique challenges in addressing such problems. We contribute towards systematically resolving some of these challenges.

In the first part of this thesis, we focus on tracking the state of a dynamical process, and develop a distributed observer for the most general class of LTI systems, linear measurement models, and time-invariant graphs. To do so, we introduce the notion of a multi-sensor observable decomposition - a generalization of the Kalman observable canonical decomposition for a single sensor. We then consider a scenario where certain agents in the network are compromised based on the classical Byzantine adversary model. For this worst-case adversarial setting, we identify certain fundamental necessary conditions that are a blend of system- and network-theoretic requirements. We then develop an attack-resilient, provably-correct, fully distributed state estimation algorithm. Finally, by drawing connections to the concept of age-of-information for characterizing information freshness, we show how our framework can be extended to handle a broad class of time-varying graphs. Notably, in each of the cases above, our proposed algorithms guarantee exponential convergence at any desired convergence rate.

In the second part of the thesis, we turn our attention to the problem of distributed hypothesis testing/inference, where each agent receives a stream of stochastic signals generated by an unknown static state that belongs to a finite set of hypotheses. To enable each agent to uniquely identify the true state, we develop a novel distributed learning rule that employs a min-protocol for data-aggregation, as opposed to the large body of existing techniques that rely on "belief-averaging". We establish consistency of our rule under minimal requirements on the observation model and the network structure, and prove that it guarantees exponentially fast convergence to the truth with probability 1. Most importantly, we establish that the learning rate of our algorithm is network-independent, and a strict improvement over all existing approaches. We also develop a simple variant of our learning algorithm that can account for misbehaving agents. As the final contribution of this work, we develop communication-efficient rules for distributed hypothesis testing. Specifically, we draw on ideas from event-triggered control to reduce the number of communication rounds, and employ an adaptive quantization scheme that guarantees exponentially fast learning almost surely, even when just 1 bit is used to encode each hypothesis.

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Books on the topic "Multi-model inference"

Burnham, Kenneth P., and David Anderson. Model Selection and Multi-Model Inference. Springer, 2003.

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Book chapters on the topic "Multi-model inference"

Tozzo, Veronica, and Annalisa Barla. "Multi-parameters Model Selection for Network Inference." In Complex Networks and Their Applications VIII, 566–77. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36687-2_47.

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Yang, Tianbao, Lei Wu, and Piero P. Bonissone. "A Directed Inference Approach towards Multi-class Multi-model Fusion." In Multiple Classifier Systems, 352–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38067-9_31.

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Wang, Jianyu, Debin Zhao, Shiguang Shan, and Wen Gao. "Approximating Inference on Complex Motion Models Using Multi-model Particle Filter." In Advances in Multimedia Information Processing - PCM 2004, 1011–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30542-2_124.

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Nickles, Matthias. "Sampling-Based SAT/ASP Multi-model Optimization as a Framework for Probabilistic Inference." In Inductive Logic Programming, 88–104. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99960-9_6.

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Nijkamp, Erik, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu. "Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference." In Computer Vision – ECCV 2020, 361–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58539-6_22.

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Lendvai, Piroska. "Towards a Discourse-driven Taxonomic Inference Model." In Interactive Multi-modal Question-Answering, 247–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17525-1_11.

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Mihailescu, Radu-Casian, Paul Davidsson, and Jan Persson. "Multiagent Model for Agile Context Inference Based on Articial Immune Systems and Sparse Distributed Representations." In Multi-Agent Systems and Agreement Technologies, 82–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33509-4_7.

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"Appendix D: Parsimony, Prediction, and Multi-Model Inference." In Decision Making in Natural Resource Management: A Structured, Adaptive Approach, 373–83. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118506196.app4.

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Jovančić, Predrag D., Miloš Tanasijević, Vladimir Milisavljević, Aleksandar Cvjetić, Dejan Ivezić, and Uglješa Srbislav Bugarić. "Applying the Fuzzy Inference Model in Maintenance Centered to Safety." In Advances in Civil and Industrial Engineering, 142–65. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3904-0.ch009.

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The main idea of this chapter is to promote maintenance centered to safety, in accordance to adaptive fuzzy inference model, which has online adjustment to working conditions. Input data for this model are quality of service indicators of analyzed engineering system: reliability, maintainability, failure consequence, and severity and detectability. Indicators in final form are obtained with permanent monitoring of the engineering system and statistical processing. Level of safety is established by composition and ranking of indicators according to fuzzy inference engine. The problem of monitoring and processing of indicators comprising safety is solved by using the features that Industry4.0 provides. Maintenance centered to safety is important for complex, multi-hierarchy engineering systems. Sudden failures on such systems could have significant financial and environmental effect. Developed model will be tested in the final part of the chapter, in the case study of bucket wheel excavator.

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Casillas, Luis, Adriana Peña, and Alfredo Gutierrez. "Towards an Automated Model to Evaluate Collaboration Through Non-Verbal Interaction in Collaborative Virtual Environments." In Intelligent Systems, 1570–86. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5643-5.ch068.

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Virtual environments represent a helpful resource for learning and training. In their multiuser modality, Collaborative Virtual Environments (CVE) support geographical distant people to experience collaborative learning and team training; a context in which the automatic monitor of collaboration can provide valuable and in time information, either for human instructors or intelligent tutor systems, about individual and group performance. CVE enable people to share a virtual space where they interact through a graphical representation, generating nonverbal behavior such as gaze-direction or deictic gestures, a potential means to understand collaboration. This paper presents an automated model and its inference mechanisms to evaluate collaboration in CVE based on the nonverbal activity of the participants. The model is a multi-layer analysis that includes: data filtering, fuzzy classification, and rule-based inference producing high-level assessment for group collaboration.

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Conference papers on the topic "Multi-model inference"

Shi, Peiqi, Feng Gao, Songtao Liang, and Shanjin Yu. "Multi-Model Inference Acceleration on Embedded Multi-Core Processors." In 2020 International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI). IEEE, 2020. http://dx.doi.org/10.1109/ichci51889.2020.00090.

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Brownlees, Christian T., Simone Contini, Riccardo Di Meo, and Valerio Sullo. "Financial Risk Management Via Multi Model Inference GRID Applications." In 1st International Workshop on Grid Technology for Financial Modeling and Simulation. Trieste, Italy: Sissa Medialab, 2007. http://dx.doi.org/10.22323/1.026.0004.

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Wang, Jin, Chen Wang, Qingming Huang, Yunhui Shi, Jian-Feng Cai, Qing Zhu, and Baocai Yin. "Image Inpainting Based on Multi-frequency Probabilistic Inference Model." In MM '20: The 28th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394171.3413891.

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Han, Qilong, Dan Lu, and Rui Chen. "Fine-Grained Air Quality Inference via Multi-Channel Attention Model." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/346.

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In this paper, we study the problem of fine-grained air quality inference that predicts the air quality level of any location from air quality readings of nearby monitoring stations. We point out the importance of explicitly modeling both static and dynamic spatial correlations, and consequently propose a novel multi-channel attention model (MCAM) that models static and dynamic spatial correlations as separate channels. The static channel combines the beauty of attention mechanisms and graph-based spatial modeling via an adapted bilateral filtering technique, which considers not only locations' Euclidean distances but also their similarity of geo-context features. The dynamic channel learns stations' time-dependent spatial influence on a target location at each time step via long short-term memory (LSTM) networks and attention mechanisms. In addition, we introduce two novel ideas, atmospheric dispersion theories and the hysteretic nature of air pollutant dispersion, to better model the dynamic spatial correlation. We also devise a multi-channel graph convolutional fusion network to effectively fuse the graph outputs, along with other features, from both channels. Our extensive experiments on real-world benchmark datasets demonstrate that MCAM significantly outperforms the state-of-the-art solutions.

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Liu, Chongyu, Rui Yao, S. Hamid Rezatofighi, Ian Reid, and Qinfeng Shi. "Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference." In 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2017. http://dx.doi.org/10.1109/dicta.2017.8227468.

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ZEITVOGEL, Samuel, and Astrid LAUBENHEIMER. "An Open-Source Articulated Multi-Person Shape Model Training and Inference Pipeline." In 3DBODY.TECH 2020 - 11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Online/Virtual, 17-18 November 2020. Ascona, Switzerland: Hometrica Consulting - Dr. Nicola D'Apuzzo, 2020. http://dx.doi.org/10.15221/20.17.

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Yan, Fu. "The design for restructuring translation model based on multi-feature inference hypothesis." In 2015 International Symposium on Computers and Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isci-15.2015.61.

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Pei, Wei, and Yong-ying Zhu. "A Multi-factor Classified Runoff Forecast Model Based on Rough Fuzzy Inference Method." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.336.

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Liu, Hao, Lirong He, Haoli Bai, Bo Dai, Kun Bai, and Zenglin Xu. "Structured Inference for Recurrent Hidden Semi-markov Model." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/339.

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Segmentation and labeling for high dimensional time series is an important yet challenging task in a number of applications, such as behavior understanding and medical diagnosis. Recent advances to model the nonlinear dynamics in such time series data, has suggested to involve recurrent neural networks into Hidden Markov Models. However, this involvement has caused the inference procedure much more complicated, often leading to intractable inference, especially for the discrete variables of segmentation and labeling. To achieve both flexibility and tractability in modeling nonlinear dynamics of discrete variables, we present a structured and stochastic sequential neural network (SSNN), which composes with a generative network and an inference network. In detail, the generative network aims to not only capture the long-term dependencies but also model the uncertainty of the segmentation labels via semi-Markov models. More importantly, for efficient and accurate inference, the proposed bi-directional inference network reparameterizes the categorical segmentation with the Gumbel-Softmax approximation and resorts to the Stochastic Gradient Variational Bayes. We evaluate the proposed model in a number of tasks, including speech modeling, automatic segmentation and labeling in behavior understanding, and sequential multi-objects recognition. Experimental results have demonstrated that our proposed model can achieve significant improvement over the state-of-the-art methods.

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Tawara, Naohiro, Tetsuji Ogawa, Shinji Watanabe, and Tetsunori Kobayashi. "Fully Bayesian inference of multi-mixture Gaussian model and its evaluation using speaker clustering." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6289105.

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Reports on the topic "Multi-model inference"

Grace, Matthew, Thomas Stephen Lowry, Bill Walter Arnold, Scott Carlton James, Genetha Anne Gray, and Michael Ahlmann. SNL-NUMO collaborative : development of a deterministic site characterization tool using multi-model ranking and inference. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/947331.

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