Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods

Agronomie ◽  
2002 ◽  
Vol 22 (2) ◽  
pp. 191-203 ◽  
Author(s):  
David Makowski ◽  
Daniel Wallach ◽  
Marie Tremblay
Keyword(s):  
Parameter Estimation ◽  
Bayesian Approach ◽  
Mcmc Methods

A Bayesian approach for parameter estimation in multi-stage models

2018 ◽  
Vol 48 (10) ◽  
pp. 2459-2482 ◽  
Author(s):  
Hoa Pham ◽  
Darfiana Nur ◽  
Huong T. T. Pham ◽  
Alan Branford
Keyword(s):  
Parameter Estimation ◽  
Bayesian Approach ◽  
Stage Models ◽  
Multi Stage

scholarly journals A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters

2016 ◽  
Vol 14 (03) ◽  
pp. 1650007 ◽  
Author(s):  
Matthias Gerstgrasser ◽  
Sarah Nicholls ◽  
Michael Stout ◽  
Katherine Smart ◽  
Chris Powell ◽  
...  
Keyword(s):  
High Throughput ◽  
Bayesian Approach ◽  
Microarray Data ◽  
Time Course ◽  
Microbial Growth ◽  
Mcmc Methods ◽  
Lag Phase ◽  
Maximum Output ◽  
Phenotype Microarray ◽  
Phenotype Microarrays

Biolog phenotype microarrays (PMs) enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo (MCMC) methods to enable high throughput estimation of important information, including length of lag phase, maximal “growth” rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.

Parameter estimation based on MCMC methods in PM2.5 and traffic

2010 ◽  
Author(s):  
Weiqiang Wang ◽  
Zhendong Niu ◽  
Yumin Zhao ◽  
Yujuan Cao ◽  
Kun Zhao
Keyword(s):  
Parameter Estimation ◽  
Mcmc Methods

scholarly journals Generalized exponential distribution: A Bayesian approach using MCMC methods

2015 ◽  
Vol 6 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Alberto Achcar ◽  
Fernando Antȏnio Moala ◽  
Juliana Boleta
Keyword(s):  
Exponential Distribution ◽  
Bayesian Approach ◽  
Mcmc Methods ◽  
Generalized Exponential Distribution

Parameter estimation of general regression neural network using Bayesian approach

2016 ◽  
Author(s):  
Achmad Syahrul Choir ◽  
Rindang Bangun Prasetyo ◽  
Brodjol Sutijo Suprih Ulama ◽  
Nur Iriawan ◽  
Kartika Fitriasari ◽  
...  
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Bayesian Approach ◽  
General Regression Neural Network ◽  
General Regression
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