Filters








2,990 Hits in 5.1 sec

ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg [article]

Brandon Bocklund, Richard Otis, Aleksei Egorov, Abdulmonem Obaied, Irina Roslyakova, Zi-Kui Liu
2019 arXiv   pre-print
The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method.  ...  ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refine the model parameters using phase equilibrium data through Bayesian  ...  Developing models and evaluating model parameters is central to the CALPHAD method, but, with the exception of some recent attempts to study the influence of model parameterization in CALPHAD models computationally  ... 
arXiv:1902.01269v1 fatcat:2niz4ncizzd2zh4hmqzgy55jzi

Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs [article]

Victor Gallego, David Rios Insua
2020 arXiv   pre-print
Experimental evidence of its efficient performance is shown solving an influence diagram in a high-dimensional space using a conditional variational autoencoder (cVAE) as a deep Bayes classifier; an unconditional  ...  A framework to boost the efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation.  ...  to solve influence diagrams.  ... 
arXiv:1908.09744v4 fatcat:wsfd2feowbe2hhv6ybecyyli2m

Operation of The Bayes Inference Engine [chapter]

K. M. Hanson, G. S. Cunningham
1999 Maximum Entropy and Bayesian Methods Garching, Germany 1998  
In the BIE calculational models are represented by a dataflow diagram that can be manipulated by the analyst in a graphical-programming environment.  ...  We demonstrate the operation of the BIE in terms of examples of two-dimensional tomographic reconstruction including uncertainty estimation.  ...  Data Flow Diagram Models are created in the Bayes Inference Engine through the graphicalprogramming interface shown in Fig. 1 .  ... 
doi:10.1007/978-94-011-4710-1_30 fatcat:ftwzxriacfevtpnaijbugrojny

An Evolutionary Monte Carlo algorithm for identifying short adjacent repeats in multiple sequences

Jin Xu, Qiwei Li, Xiaodan Fan, Victor O. K. Li, Shuo-Yen Robert Li
2010 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)  
Our proposed EMC scheme is implemented on a parallel platform and the simulation results show that, compared with the conventional MCMC algorithm, EMC not only improves the quality of final solution but  ...  Evolutionary Monte Carlo (EMC) algorithm is an effective and powerful method to sample complicated distributions.  ...  ACKNOWLEDGMENT This work was supported in part by the Strategic Research Theme of Information Technology of The University of Hong Kong.  ... 
doi:10.1109/bibm.2010.5706645 dblp:conf/bibm/XuLFLL10 fatcat:jkglpcnnmbg2tco7i774rvndqu

A statistical overview and perspectives on data assimilation for marine biogeochemical models

Michael Dowd, Emlyn Jones, John Parslow
2014 Environmetrics  
Figure 1 shows a schematic diagram of the BGC system and describes the interactions between the system components.  ...  methods for joint state and parameter estimation).  ...  The symposium funding was supplied by the CSIRO office of the Chief Executive through the Cutting Edge Symposium fund; additional funds were supplied by the CSIRO Computational and Simulation Sciences  ... 
doi:10.1002/env.2264 fatcat:lefxio3onjaxtjawe3mjlnzg2m

Bayesian Updating of Soil–Water Character Curve Parameters Based on the Monitor Data of a Large-Scale Landslide Model Experiment

Chengxin Feng, Bin Tian, Xiaochun Lu, Michael Beer, Matteo Broggi, Sifeng Bi, Bobo Xiong, Teng He
2020 Applied Sciences  
(MCMC) method.  ...  The results show that the Bayesian updating method is feasible for the monitoring of data of large-scale landslide model experiments.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app10165526 fatcat:rvofjf42pffazpd23ihag2v7ki

ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu–Mg

Brandon Bocklund, Richard Otis, Aleksei Egorov, Abdulmonem Obaied, Irina Roslyakova, Zi-Kui Liu
2019 MRS Communications  
The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method.  ...  ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refines the model parameters using phase equilibrium data through Bayesian  ...  Developing models and evaluating model parameters is central to the CALPHAD method, but, with the exception of some recent attempts to study the influence of model parameterization in CALPHAD models computationally  ... 
doi:10.1557/mrc.2019.59 fatcat:b52673oltfeuxjnzsuw4jc4o64

MCMC-based tracking and identification of leaders in groups

Avishy Y. Carmi, Lyudmila Mihaylova, Francois Septier, Sze Kim Pang, Pini Gurfil, Simon J. Godsill
2011 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)  
Further, the reasoning paradigm is made robust to multiple emissions and clutter by employing a class of recently introduced Markov chain Monte Carlo-based group tracking methods.  ...  Examples are provided that demonstrate the strong potential of the proposed scheme in identifying actual leaders in swarms of interacting agents and moving crowds. Avishy Y.  ...  Sequential Inference Using MCMC-Based PF The group tracking problems discussed above can be efficiently solved via the MCMC-based particle method initially proposed for solution of group tracking problems  ... 
doi:10.1109/iccvw.2011.6130232 dblp:conf/iccvw/CarmiMSPGG11 fatcat:djxdb55btrggtnckrm4uviz7wu

Sensitivity and Uncertainty Analysis of Variable-Volume Deterministic Model for Endothermic Continuously Stirred Tank Reactor

Jean Pierre Muhirwa, Isambi Sailon Mbalawata, Verdiana Grace Masanja
2020 Journal of Mathematics and Informatics  
The identifiability of physical parameters of the formulated model is done by using the least squares and the delayed rejection adaptive algorithm version of the Markov chain Monte Carlo (MCMC) method.  ...  The least square estimates are used as prior information for the MCMC method.  ...  Markov chain Monte Carlo method Markov chain Monte Carlo (MCMC) method is among the recent advanced sampling techniques developed to tackle the estimation of parameters of complex systems such as biological  ... 
doi:10.22457/jmi.v20a08189 fatcat:qsk2bxmh5bekhe67b2fnbicche

Modelling dependable systems using hybrid Bayesian networks

Martin Neil, Manesh Tailor, David Marquez, Norman Fenton, Peter Hearty
2008 Reliability Engineering & System Safety  
We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems.  ...  We illustrate its use in the field of dependability with two example of reliability estimation.  ...  alternative and perhaps better solutions to those provided by other approximate methods such as MCMC.  ... 
doi:10.1016/j.ress.2007.03.009 fatcat:cpb32eocsnbozk4qopgwuk7rma

Do inverse ecosystem models accurately reconstruct plankton trophic flows? Comparing two solution methods using field data from the California Current

Michael R. Stukel, Michael R. Landry, Mark D. Ohman, Ralf Goericke, Ty Samo, Claudia R. Benitez-Nelson
2012 Journal of Marine Systems  
New Markov Chain Monte Carlo (MCMC) solution methods have also called into question the biases of the commonly used L 2 minimum norm (L 2 MN) solution technique.  ...  Both the MCMC and L 2 MN methods predicted well-constrained rates of protozoan and mesozooplankton grazing with reasonable accuracy, but the MCMC method overestimated primary production.  ...  Barbeau for insightful comments on an early draft and D. van Oevelen and an anonymous referee who provided thoughtful reviews of this manuscript.  ... 
doi:10.1016/j.jmarsys.2011.09.004 fatcat:akkrzutju5aalgobwso4dyo27e

Adjoint-accelerated statistical and deterministic inversion of atmospheric contaminant transport

Devina Sanjaya, Krzysztof Fidkowski, Ian Tobasco
2014 Computers & Fluids  
The algorithms presented are accelerated through discrete adjoint solutions that are pre-computed efficiently in an offline stage, yielding savings in the time-critical online stage of several orders of  ...  The underlying equations of contaminant transport are assumed linear but unsteady and defined over complex geometries.  ...  influence the results.  ... 
doi:10.1016/j.compfluid.2014.05.021 fatcat:ebiskm5na5fapodg3g6v2lfysa

Four key challenges in infectious disease modelling using data from multiple sources

Daniela De Angelis, Anne M. Presanis, Paul J. Birrell, Gianpaolo Scalia Tomba, Thomas House
2015 Epidemics  
Policy makers rely on complex models that are required to be robust, realistically approximating epidemics and consistent with all relevant data.  ...  Meeting these requirements in a statistically rigorous and defendable manner poses a number of challenging problems.  ...  Liu and West, 2009) , either individually or in combination with MCMC, has allowed a start in tackling efficient estimation of complex models, with approximate methods of inference taking a central role  ... 
doi:10.1016/j.epidem.2014.09.004 pmid:25843390 pmcid:PMC4383805 fatcat:zm2khh5mgbh7tlpmio7fxtlcrq

Bayesian Exploration of Multivariate Orographic Precipitation Sensitivity for Moist Stable and Neutral Flows

Samantha A. Tushaus, Derek J. Posselt, M. Marcello Miglietta, Richard Rotunno, Luca Delle Monache
2015 Monthly Weather Review  
Exploration of the multivariate sensitivity of rainfall to changes in parameters also reveals a nonunique solution: multiple combinations of flow, topography, and environment produce similar surface rainfall  ...  Research presented here extends earlier studies by utilizing a Bayesian Markov chain Monte Carlo (MCMC) algorithm to create a large ensemble of simulations, all of which produce precipitation concentrated  ...  The comments of three reviewers served to greatly improve the presentation of our results. This work was supported by National Science Foundation Physical and Dynamic Meteorology Grant AGS 1005454.  ... 
doi:10.1175/mwr-d-15-0036.1 fatcat:ybqfh7igcvemhbbxdnuw2u64ia

Parameter Estimation Methods for Chaotic Intercellular Networks

Inés P. Mariño, Ekkehard Ullner, Alexey Zaikin, Jürgen Kurths
2013 PLoS ONE  
The first method is a stochastic optimization algorithm, known as accelerated random search method, and the other two techniques are based on approximate Bayesian computation.  ...  The first method based on approximate Bayesian computation is a Markov Chain Monte Carlo scheme that generates a series of random parameter realizations for which a low synchronization error is guaranteed  ...  The solid blue line is the complexity of the ABC MCMC algorithm (with~0:001). The solid red line is the complexity of the ARS method.  ... 
doi:10.1371/journal.pone.0079892 pmid:24282513 pmcid:PMC3839924 fatcat:imdbkf3xyjb6xozbwdzvhma5q4
« Previous Showing results 1 — 15 out of 2,990 results