Using capture-recapture data and hybrid Monte Carlo sampling to estimate an animal population affected by an environmental catastrophe

2011 ◽  
Vol 55 (1) ◽  
pp. 655-666 ◽  
Author(s):  
Guoqi Qian ◽  
Ning Li ◽  
Richard Huggins
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Animal Population ◽  
Hybrid Monte Carlo ◽  
Capture Recapture

scholarly journals Combining stochastic and deterministic approaches within high efficiency molecular simulations

2013 ◽  
Vol 11 (4) ◽  
Author(s):  
Bruno Escribano ◽  
Elena Akhmatskaya ◽  
Jon Mujika
Keyword(s):  
Molecular Dynamics ◽  
Monte Carlo ◽  
High Efficiency ◽  
Rare Event ◽  
Molecular Simulations ◽  
Monte Carlo Sampling ◽  
Symplectic Integrators ◽  
Computational Time ◽  
Hybrid Monte Carlo ◽  
Event Dynamics

AbstractGeneralized Shadow Hybrid Monte Carlo (GSHMC) is a method for molecular simulations that rigorously alternates Monte Carlo sampling from a canonical ensemble with integration of trajectories using Molecular Dynamics (MD). While conventional hybrid Monte Carlo methods completely re-sample particle’s velocities between MD trajectories, our method suggests a partial velocity update procedure which keeps a part of the dynamic information throughout the simulation. We use shadow (modified) Hamiltonians, the asymptotic expansions in powers of the discretization parameter corresponding to timestep, which are conserved by symplectic integrators to higher accuracy than true Hamiltonians. We present the implementation of this method into the highly efficient MD code GROMACS and demonstrate its performance and accuracy on computationally expensive systems like proteins in comparison with the molecular dynamics techniques already available in GROMACS. We take advantage of the state-of-the-art algorithms adopted in the code, leading to an optimal implementation of the method. Our implementation introduces virtually no overhead and can accurately recreate complex biological processes, including rare event dynamics, saving much computational time compared with the conventional simulation methods.

scholarly journals A Hybrid Monte Carlo Sampling Filter for Non-Gaussian Data Assimilation

2015 ◽  
Vol 1 (1) ◽  
pp. 41-78 ◽  
Author(s):  
Ahmed Attia ◽  
◽  
Adrian Sandu
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Monte Carlo Sampling ◽  
Hybrid Monte Carlo ◽  
Non Gaussian ◽  
Gaussian Data

scholarly journals The reduced-order hybrid Monte Carlo sampling smoother

2016 ◽  
Vol 83 (1) ◽  
pp. 28-51 ◽  
Author(s):  
Ahmed Attia ◽  
Răzvan Ştefănescu ◽  
Adrian Sandu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Hybrid Monte Carlo ◽  
Reduced Order
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