Academic literature on the topic 'MCMC data association'
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Journal articles on the topic "MCMC data association"
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Butts, Carter T. "8. Permutation Models for Relational Data." Sociological Methodology 37, no. 1 (August 2007): 257–81. http://dx.doi.org/10.1111/j.1467-9531.2007.00183.x.
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A common problem in sociology, psychology, biology, geography, and management science is the comparison of dyadic relational structures (i.e., graphs). Where these structures are formed on a common set of elements, a natural question that arises is whether there is a tendency for elements that are strongly connected in one set of structures to be more—or less—strongly connected within another set. We may ask, for instance, whether there is a correspondence between golf games and business deals, trade and warfare, or spatial proximity and genetic similarity. In each case, the data for such comparisons may be continuous or discrete, and multiple relations may be involved simultaneously (e.g., when comparing multiple measures of international trade volume with multiple types of political interactions). We propose here an exponential family of permutation models that is suitable for inferring the direction and strength of association among dyadic relational structures. A linear-time algorithm is shown for MCMC simulation of model draws, as is the use of simulated draws for maximum likelihood estimation (MCMC-MLE) and/or estimation of Monte Carlo standard errors. We also provide an easily performed maximum pseudo-likelihood estimation procedure for the permutation model family, which provides a reasonable means of generating seed models for the MCMC-MLE procedure. Use of the modeling framework is demonstrated via an application involving relationships among managers in a high-tech firm.
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Kulmon, Pavel. "Reversible Jump MCMC for Deghosting in MSPSR Systems." Sensors 21, no. 14 (July 14, 2021): 4815. http://dx.doi.org/10.3390/s21144815.
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This paper deals with bistatic track association and deghosting in the classical frequency modulation (FM)-based multi-static primary surveillance radar (MSPSR). The main contribution of this paper is a novel algorithm for bistatic track association and deghosting. The proposed algorithm is based on a hierarchical model which uses the Indian buffet process (IBP) as the prior probability distribution for the association matrix. The inference of the association matrix is then performed using the classical reversible jump Markov chain Monte Carlo (RJMCMC) algorithm with the usage of a custom set of the moves proposed by the sampler. A detailed description of the moves together with the underlying theory and the whole model is provided. Using the simulated data, the algorithm is compared with the two alternative ones and the results show the significantly better performance of the proposed algorithm in such a simulated setup. The simulated data are also used for the analysis of the properties of Markov chains produced by the sampler, such as the convergence or the posterior distribution. At the end of the paper, further research on the proposed method is outlined.
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Ruffieux, Helene, Anthony C. Davison, Jorg Hager, and Irina Irincheeva. "Efficient inference for genetic association studies with multiple outcomes." Biostatistics 18, no. 4 (March 16, 2017): 618–36. http://dx.doi.org/10.1093/biostatistics/kxx007.
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SUMMARY Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons. Inspired by Richardson and others (2010, Bayesian Statistics 9), we consider a sparse multivariate regression model that allows simultaneous selection of predictors and associated responses. As Markov chain Monte Carlo (MCMC) inference on such models can be prohibitively slow when the number of genetic variants exceeds a few thousand, we propose a variational inference approach which produces posterior information very close to that of MCMC inference, at a much reduced computational cost. Extensive numerical experiments show that our approach outperforms popular variable selection methods and tailored Bayesian procedures, dealing within hours with problems involving hundreds of thousands of genetic variants and tens to hundreds of clinical or molecular outcomes.
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Ayres, Karen L., and David J. Balding. "Measuring Gametic Disequilibrium From Multilocus Data." Genetics 157, no. 1 (January 1, 2001): 413–23. http://dx.doi.org/10.1093/genetics/157.1.413.
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Abstract We describe a Bayesian approach to analyzing multilocus genotype or haplotype data to assess departures from gametic (linkage) equilibrium. Our approach employs a Markov chain Monte Carlo (MCMC) algorithm to approximate the posterior probability distributions of disequilibrium parameters. The distributions are computed exactly in some simple settings. Among other advantages, posterior distributions can be presented visually, which allows the uncertainties in parameter estimates to be readily assessed. In addition, background knowledge can be incorporated, where available, to improve the precision of inferences. The method is illustrated by application to previously published datasets; implications for multilocus forensic match probabilities and for simple association-based gene mapping are also discussed.
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Shalfawi, Shaher A. I. "Bayesian Estimation of Correlation between Measures of Blood Pressure Indices, Aerobic Capacity and Resting Heart Rate Variability Using Markov Chain Monte Carlo Simulation and 95% High Density Interval in Female School Teachers." International Journal of Environmental Research and Public Health 17, no. 18 (September 16, 2020): 6750. http://dx.doi.org/10.3390/ijerph17186750.
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Background: Several explanations regarding the disparity observed in the literature with regard to heart rate variability (HRV) and its association with performance parameters have been proposed: the time of day when the recording was conducted, the condition (i.e., rest, active, post activity) and the mathematical and physiological relationships that could have influenced the results. A notable observation about early studies is that they all followed the frequentist approach to data analyses. Therefore, in an attempt to explain the disparity observed in the literature, the primary purpose of this study was to estimate the association between measures of HRV indices, aerobic performance parameters and blood pressure indices using the Bayesian estimation of correlation on simulated data using Markov Chain Monte Carlo (MCMC) and the equal probability of the 95% high density interval (95% HDI). Methods: The within-subjects with a one-group pretest experimental design was chosen to investigate the relationship between baseline measures of HRV (rest; independent variable), myocardial work (rate–pressure product (RPP)), mean arterial pressure (MAP) and aerobic performance parameters. The study participants were eight local female schoolteachers aged 54.1 ± 6.5 years (mean ± SD), with a body mass of 70.6 ± 11.5 kg and a height of 164.5 ± 6.5 cm. Their HRV data were analyzed in R package, and the Bayesian estimation of correlation was calculated employing the Bayesian hierarchical model that uses MCMC simulation integrated in the JAGS package. Results: The Bayesian estimation of correlation using MCMC simulation reproduced and supported the findings reported regarding norms and the within-HRV-indices associations. The results of the Bayesian estimation showed a possible association (regardless of the strength) between pNN50% and MAP (rho = 0.671; 95% HDI = 0.928–0.004), MeanRR (ms) and RPP (rho = −0.68; 95% HDI = −0.064–−0.935), SDNN (ms) and RPP (rho = 0.672; 95% HDI = 0.918–0.001), LF (ms2) and RPP (rho = 0.733; 95% HDI = 0.935–0.118) and SD2 and RPP (rho = 0.692; 95% HDI = 0.939–0.055). Conclusions: The Bayesian estimation of correlation with 95% HDI on MCMC simulated data is a new technique for data analysis in sport science and seems to provide a more robust approach to allocating credibility through a meaningful mathematical model. However, the 95% HDI found in this study, accompanied by the theoretical explanations regarding the dynamics between the parasympathetic nervous system and the sympathetic nervous system in relation to different recording conditions (supine, reactivation, rest), recording systems, time of day (morning, evening, sleep etc.) and age of participants, suggests that the association between measures of HRV indices and aerobic performance parameters has yet to be explicated.
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Khan, Z., T. Balch, and F. Dellaert. "MCMC Data Association and Sparse Factorization Updating for Real Time Multitarget Tracking with Merged and Multiple Measurements." IEEE Transactions on Pattern Analysis and Machine Intelligence 28, no. 12 (December 2006): 1960–72. http://dx.doi.org/10.1109/tpami.2006.247.
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Kokkala, Juho, and Simo Särkkä. "Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking." Digital Signal Processing 47 (December 2015): 84–95. http://dx.doi.org/10.1016/j.dsp.2015.04.004.
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Thi Thu Pham, Huong, Hoa Pham, and Darfiana Nur. "A Bayesian inference for the penalized spline joint models of longitudinal and time-to-event data: A prior sensitivity analysis." Monte Carlo Methods and Applications 26, no. 1 (March 1, 2020): 49–68. http://dx.doi.org/10.1515/mcma-2020-2058.
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AbstractBayesian approaches have been used in the literature to estimate the parameters for joint models of longitudinal and time-to-event data. The main aim of this paper is to analyze the impact of prior distributions on estimating parameters in a proposed fully Bayesian analysis setting for the penalized spline joint models. To achieve this aim, the joint posterior distribution of parameters in survival and longitudinal submodels is presented. The Markov chain Monte Carlo (MCMC) algorithm is then proposed, which consists of the Gibbs sampler (GS) and Metropolis Hastings (MH) algorithms to sample for the target conditional posterior distributions. The prior sensitivity analysis for the baseline hazard rate and association parameters is performed through simulation studies and a case study.
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Gokul, T., and M. R. Srinivasan. "Joint Modeling for Longitudinal Data with Missing Values: A Bayesian Perspective on Human Intelligence." Journal of Scientific Research 13, no. 2 (May 1, 2021): 521–36. http://dx.doi.org/10.3329/jsr.v13i2.50479.
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Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.
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Pham, Thi Mui, and Maria Kateri. "Inference for Ordinal Log-Linear Models Based on Algebraic Statistics." Journal of Algebraic Statistics 10, no. 1 (April 10, 2019): 30–50. http://dx.doi.org/10.18409/jas.v10i1.74.
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Tools of algebraic statistics combined with MCMC algorithms have been used in contingency table analysis for model selection and model fit testing of log-linear models. However, this approach has not been considered so far for association models, which are special log-linear models for tables with ordinal classification variables. The simplest association model for two-way tables, the uniform (U) association model, has just one parameter more than the independence model and is applicable when both classification variables are ordinal. Less parsimonious are the row (R) and column (C) effect association models, appropriate when at least one of the classification variables is ordinal. Association models have been extended for multidimensional contingency tables as well. Here, we adjust algebraic methods for association models analysis and investigate their eligibility, focusing mainly on two-way tables. They are implemented in the statistical software R and illustrated on real data tables. Finally the algebraic model fit and selection procedure is assessed and compared to the asymptotic approach in terms of a simulation study.
Dissertations / Theses on the topic "MCMC data association"
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Brau, Avila Ernesto. "Bayesian Data Association for Temporal Scene Understanding." Diss., The University of Arizona, 2013. http://hdl.handle.net/10150/312653.
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Understanding the content of a video sequence is not a particularly difficult problem for humans. We can easily identify objects, such as people, and track their position and pose within the 3D world. A computer system that could understand the world through videos would be extremely beneficial in applications such as surveillance, robotics, biology. Despite significant advances in areas like tracking and, more recently, 3D static scene understanding, such a vision system does not yet exist. In this work, I present progress on this problem, restricted to videos of objects that move in smoothly and which are relatively easily detected, such as people. Our goal is to identify all the moving objects in the scene and track their physical state (e.g., their 3D position or pose) in the world throughout the video. We develop a Bayesian generative model of a temporal scene, where we separately model data association, the 3D scene and imaging system, and the likelihood function. Under this model, the video data is the result of capturing the scene with the imaging system, and noisily detecting video features. This formulation is very general, and can be used to model a wide variety of scenarios, including videos of people walking, and time-lapse images of pollen tubes growing in vitro. Importantly, we model the scene in world coordinates and units, as opposed to pixels, allowing us to reason about the world in a natural way, e.g., explaining occlusion and perspective distortion. We use Gaussian processes to model motion, and propose that it is a general and effective way to characterize smooth, but otherwise arbitrary, trajectories. We perform inference using MCMC sampling, where we fit our model of the temporal scene to data extracted from the videos. We address the problem of variable dimensionality by estimating data association and integrating out all scene variables. Our experiments show our approach is competitive, producing results which are comparable to state-of-the-art methods.
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Niedfeldt, Peter C. "Recursive-RANSAC: A Novel Algorithm for Tracking Multiple Targets in Clutter." BYU ScholarsArchive, 2014. https://scholarsarchive.byu.edu/etd/4195.
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Multiple target tracking (MTT) is the process of identifying the number of targets present in a surveillance region and the state estimates, or track, of each target. MTT remains a challenging problem due to the NP-hard data association step, where unlabeled measurements are identified as either a measurement of an existing target, a new target, or a spurious measurement called clutter. Existing techniques suffer from at least one of the following drawbacks: divergence in clutter, underlying assumptions on the number of targets, high computational complexity, time-consuming implementation, poor performance at low detection rates, and/or poor track continuity. Our goal is to develop an efficient MTT algorithm that is simple yet effective and that maintains track continuity enabling persistent tracking of an unknown number of targets. A related field to tracking is regression analysis, where the parameters of static signals are estimated from a batch or a sequence of data. The random sample consensus (RANSAC) algorithm was developed to mitigate the effects of spurious measurements, and has since found wide application within the computer vision community due to its robustness and efficiency. The main concept of RANSAC is to form numerous simple hypotheses from a batch of data and identify the hypothesis with the most supporting measurements. Unfortunately, RANSAC is not designed to track multiple targets using sequential measurements.To this end, we have developed the recursive-RANSAC (R-RANSAC) algorithm, which tracks multiple signals in clutter without requiring prior knowledge of the number of existing signals. The basic premise of the R-RANSAC algorithm is to store a set of RANSAC hypotheses between time steps. New measurements are used to either update existing hypotheses or generate new hypotheses using RANSAC. Storing multiple hypotheses enables R-RANSAC to track multiple targets. Good tracks are identified when a sufficient number of measurements support a hypothesis track. The complexity of R-RANSAC is shown to be squared in the number of measurements and stored tracks, and under moderate assumptions R-RANSAC converges in mean to the true states. We apply R-RANSAC to a variety of simulation, camera, and radar tracking examples.
Conference papers on the topic "MCMC data association"
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Yu, Qian, and Gerard Medioni. "Integrated Detection and Tracking for Multiple Moving Objects using Data-Driven MCMC Data Association." In 2008 IEEE Workshop on Motion and video Computing (WMVC). IEEE, 2008. http://dx.doi.org/10.1109/wmvc.2008.4544066.
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Ackermann, Dimitri, and Georg Schmitz. "Reconstruction of flow velocity inside vessels by tracking single microbubbles with an MCMC data association algorithm." In 2013 IEEE International Ultrasonics Symposium (IUS). IEEE, 2013. http://dx.doi.org/10.1109/ultsym.2013.0162.
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Jardine, Leslie J., Georg B. Borisov, Sergey I. Rovny, Konstantin G. Kudinov, and Alexander A. Shvedov. "An Opportunity to Immobilize 1.6 MT or More of Weapons-Grade Plutonium at the Mayak and Krasnoyarsk-26 Sites." In ASME 2001 8th International Conference on Radioactive Waste Management and Environmental Remediation. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/icem2001-1272.
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Abstract The Mayak Production Association (PA Mayak), an industrial site in Russia, will be assigned multiple new plutonium disposition missions in order to implement the Agreement Between The Government Of The United States Of America And The Government Of Russian Federation Concerning The Management And Disposition Of Plutonium Designated As No Longer Required For Defense Purposes And Related Cooperation signed September 1, 2000, by Gore and Kasyanov, In addition, the mission of industrial-scale mixed-oxide (MOX) fabrication will be assigned to either the Mining Chemical Combine (MCC) industrial site at Krasnoyarsk-26 (K-26) or PA Mayak. Over the next decades, these new missions will generate radioactive wastes containing weapons-grade plutonium. The existing Mayak and K-26 onsite facilities and infrastructures cannot currently treat and immobilize these Pu-containing wastes for storage and disposal. However, the wastes generated under the Agreement must be properly immobilized, treated, and managed. New waste treatment and immobilization missions at Mayak may include operating facilities for plutonium metal-to-oxide conversion processes, industrial-scale MOX fuel fabrication, BN-600 PAKET hybrid core MOX fuel fabrication, and a plutonium conversion demonstration process. The MCC K-26 site, if assigned the industrial-scale MOX fuel fabrication mission, would also need to add facilities to treat and immobilize the Pu-containing wastes. This paper explores the approach and cost of treatment and immobilization facilities at both Mayak and K-26. The current work to date at Mayak and MCC K-26 indicates that the direct immobilization of 1.6 MT of weapons-grade plutonium is a viable and cost-effective alternative.