Results 41 to 50 of about 135,414 (287)

Biopolymer Structure Simulation and Optimization via Fragment Regrowth Monte Carlo [PDF]

, 2007
An efficient exploration of the configuration space of a biopolymer is essential for its structure modeling and prediction. In this study, the authors propose a new Monte Carlo method, fragment regrowth via energy-guided sequential sampling (FRESS ...
Kou, Samuel, Liu, Jun, Zhang, Jinfeng
core   +1 more source

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

Frontiers in Genetics, 2021
Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures.
Kaixian Yu, Zihan Cui, Xin Sui, Xing Qiu, Jinfeng Zhang   +4 more
doaj   +1 more source

Capacity estimation of two-dimensional channels using Sequential Monte Carlo [PDF]

, 2014
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-infinity run-length limited constrained channel, but the underlying idea is
Lindsten, Fredrik, Naesseth, Christian A., Schön, Thomas B.   +2 more
core   +1 more source

Model Selection and Parameter Estimation for an Improved Approximate Bayesian Computation Sequential Monte Carlo Algorithm

Discrete Dynamics in Nature and Society, 2022
Model selection and parameter estimation are very important in many fields. However, the existing methods have many problems, such as low efficiency in model selection and inaccuracy in parameter estimation.
Yue Deng, Yongzhen Pei, Changguo Li, Bin Zhu   +3 more
doaj   +1 more source

Rare isotope production in statistical multifragmentation [PDF]

, 2000
Producing rare isotopes through statistical multifragmentation is investigated using the Mekjian method for exact solutions of the canonical ensemble. Both the initial fragmentation and the the sequential decay are modeled in such a way as to avoid Monte
A. Majumder, A. S. Botvina, Christopher Morling, D. H. E. Gross, D. H. E. Gross, E. Plagnol, G. Fai, G. Kortemeyer, H. M. Harreis, H. Müller, H. S. Xu, J. Cugnon, J. P. Bondorf, J. Pan, K. C. Chase, K. C. Chase, K. C. Chase, P. Bhattacharyya, P. Möller, Patrick Underhill, Ph. Chomaz, S. Das Gupta, S. K. Samaddar, S. Pratt, Scott Pratt, Wolfgang Bauer   +25 more
core   +2 more sources

Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization [PDF]

Hydrology and Earth System Sciences, 2011
Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process ...
S. J. Noh, Y. Tachikawa, M. Shiiba, S. Kim   +3 more
doaj   +1 more source

Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces

, 2015
Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as ...
Peters, Gareth W., Septier, Francois
core   +3 more sources

Automated FRAP microscopy for high‐throughput analysis of protein dynamics in chromatin organization and transcription

FEBS Open Bio, EarlyView.
RoboMic is an automated confocal microscopy pipeline for high‐throughput functional imaging in living cells. Demonstrated with fluorescence recovery after photobleaching (FRAP), it integrates AI‐driven nuclear segmentation, ROI selection, bleaching, and analysis.
Selçuk Yavuz, Bart Geverts, Johan A. Slotman, Andrea Sacchetti, Stefan Prekovic, Martin E. van Royen, Adriaan B. Houtsmuller   +6 more
wiley   +1 more source

Energy-Adaptive SGHSMC: A Particle-Efficient Nonlinear Filter for High-Maneuver Target Tracking

Mathematics
Tracking targets with nonlinear motion patterns remains a significant challenge in state estimation. We propose an energy-adaptive stochastic gradient Hamiltonian sequential Monte Carlo (SGHSMC) filter that combines adaptive energy dynamics with ...
Chang Ho Kang, Sun Young Kim
doaj   +1 more source

Reliability Evaluation of Tidal Current Farm Integrated Generation Systems Considering Wake Effects

IEEE Access, 2018
Based on a sequential Monte-Carlo simulation technique, a reliability evaluation method and several indices are proposed for tidal current farm integrated generation systems in this paper.
Zhouyang Ren, Hui Li, Wenyuan Li, Xueqian Zhao, Yihao Sun, Te Li, Fan Jiang   +6 more
doaj   +1 more source