A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Raveendran, Nishanthi; Sofronov, Georgy

doi:10.3390/info12020058

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…Spatial segmentation algorithms are widely used in many areas, including spatial epidemiology (Gangnon & Clayton (2000)), ecology (see, for example, Beckage et al (2007), López et al (2010), Raveendran & Sofronov (2017)), climatology (Tripathi & Govindaraju (2009)) and economic applications (Arbia et al (2008), Cai et al (2016)). For example, similar spatial segmentation problems were recently studied by Raveendran & Sofronov (2019, 2021 to identify homogeneous spatial domains in lattice data. To solve this two-dimensional segmentation problem, we propose to use the multiple change point detection methodology, which is commonly used to detect change points and their locations in linear data arising in a wide range of applications such as genomics, economics, climatology, and bioinformatics (Evans et al (2011), Polushina & Sofronov (2011, 2013, Priyadarshana & Sofronov (2012, 2014, Sofronov et al (2009)).…”

Section: Introductionmentioning

confidence: 99%

A binary segmentation method for detecting topological domains in Hi-C data

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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The three-dimensional (3D) architecture of chromosomes in nuclear space plays an important role in studying gene expression and regulation in cell biology. In particular, chromosome conformation capture (3C) techniques are used to study the spatial structure of chromosomes. Many such methods have been developed in the last two decades. Among them, Hi-C is a technology that uses a deep sequencing approach to detect the 3D spatial organization of a genome. That is, Hi-C allows us to evaluate spatial proximity between any pair of loci along the genome. This results in an interaction matrix with the frequency of interactions between genomic loci that physically interact in the nucleus. Highly self-interacting regions appear in the contact map of such interaction matrices. These regions are called topological domains and they play an important role in regulating gene expression and other genomic functions. Thus detecting such topological domains will provide new insights on chromosomal conformation in better understanding of cell functioning and various diseases.The topological domains centered along a diagonal region in contact maps are more likely to exist and prominent in data. In this study, we focus on detecting such domains, and we approach this problem as a twodimensional segmentation problem. To solve this segmentation problem, we propose an algorithm based on the binary segmentation method, a well-known recursive partitioning technique used in change point detection problems. Our numerical experiments illustrate the usefulness of this approach. We obtain estimates for the number of diagonal blocks and their boundaries in an artificially generated data matrix and compare the results of these estimates to those obtained with the HiCSeg R package. We conclude that binary segmentation method works well in identifying such domains with easy implementation and a low computational cost.

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Section: Introductionmentioning

confidence: 99%

A binary segmentation method for detecting topological domains in Hi-C data

2023

MODSIM2023, 25th International Congress on Modelling and Simulation.

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“…The MCMC algorithms have emerged in recent years and have been widely used for numerical computation. Because the MCMC algorithms are suitable for simulating distributions that are multivariate and non-standard or even have mutually dependent variables, they are applicable to reservoir prediction [27][28][29][30]. Their implementation involves the following steps.…”

Section: Introductionmentioning

confidence: 99%

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

Dong

Gui

et al. 2023

Processes

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To improve the prediction of thin reservoirs with special geophysical responses, a geostatistical inversion technique is proposed based on an in-depth analysis of the theory of geostatistical inversion. This technique is based on the Markov chain Monte Carlo algorithm, to which we added the contents of facies-constrained. The feasibility of the technique and the reliability of the prediction results are demonstrated by a prediction of the sand bodies in the braided river channel bars in the Xiazijie Oilfield in the Junggar Basin. Based on the MCMC algorithm, the results show that leveraging the lateral changes in the seismic waveforms as geologically relevant information to drive the construction of the variogram and the optimization of the statistical sampling can largely overcome the obstacle that prevents traditional geostatistical inversions from accurately delineating the sedimentary characteristics; thereby, the proposed algorithm truly achieves facies-constrained geostatistical inversion. The case study of the Xiazijie Oilfield showed the feasibility and reliability of this technology. The prediction accuracy of the FCMCMC algorithm-based geostatistical inversion is as high as 6 m for thin interbedded reservoirs, and the coincidence rate between the prediction results and the well log data is more than 85%, which confirms the reliability of the technique. The demonstrated performance of the proposed technique provides a preliminary reference for the prediction of the thin interbedded reservoirs formed in terrestrial sedimentary basins and characterized by small thicknesses and rapid lateral changes with special geophysical responses.

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A Bayesian Pipe Failure Prediction for Optimizing Pipe Renewal Time in Water Distribution Networks

Nugroho

Utomo²,

Iriawan³

2022

Infrastructures

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The sustainable management of the water supply system requires methodologies to monitor, repair, or replace the aging infrastructure, but more importantly, it must be able to assess the condition of the networks and predict their behavior over time. Among other infrastructure systems, the water distribution network is one of the essential civil infrastructure systems; therefore, the effective maintenance and renewal of the infrastructure’s physical assets are essential. This article aims to determine pipe failure prediction to optimize pipe renewal time. This research methodology investigates the most appropriate parameters for predicting pipe failure in the optimization. In particular, the non-homogeneous Poisson process (NHPP) with the Markov chain Monte Carlo (MCMC) approach is presented for Bayesian inference, while maximum likelihood (ML) is applied for frequentist inference as a comparison method. It is concluded that the two estimations are relatively appropriate for predicting failures, but MCMC estimation is closer to the total observed data. Based on life-cycle cost (LCC) analysis, the MCMC estimation generates flatter LCC curves and lower LCC values than the ML estimation, which affects the decision making of optimum pipe renewal in water distribution networks.

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A Markov Chain Monte Carlo Algorithm for Spatial Segmentation

Cited by 3 publications

References 36 publications

A binary segmentation method for detecting topological domains in Hi-C data

A binary segmentation method for detecting topological domains in Hi-C data

Theory and Application of Geostatistical Inversion: A Facies-Constrained MCMC Algorithm

A Bayesian Pipe Failure Prediction for Optimizing Pipe Renewal Time in Water Distribution Networks

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