A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
ESPEI for efficient thermodynamic database development, modification, and uncertainty quantification: application to Cu-Mg
[article]
2019
arXiv
pre-print
Preserved Fulltext
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
1999
Maximum Entropy and Bayesian Methods Garching, Germany 1998
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2004; you can also visit the original URL.
The file type is application/pdf.
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
2010
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2014
Environmetrics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
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
2020
Applied Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
(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
2019
MRS Communications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2011
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
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
2020
Journal of Mathematics and Informatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2008
Reliability Engineering & System Safety
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
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
2012
Journal of Marine Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2014
Computers & Fluids
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
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
2015
Epidemics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2015
Monthly Weather Review
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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
2013
PLoS ONE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
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