A maximum likelihood approximation method for Dirichlet's parameter estimation

2008 ◽  
Vol 52 (3) ◽  
pp. 1315-1322 ◽  
Author(s):  
Nicolas Wicker ◽  
Jean Muller ◽  
Ravi Kiran Reddy Kalathur ◽  
Olivier Poch
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Approximation Method ◽  
Likelihood Approximation

The extended algorithm for quasi maximum likelihood parameter estimation

2021 ◽  
Author(s):  
Alfira Mulya Astuti ◽  
Setiawan ◽  
Ismaini Zain ◽  
Jerry D. T. Purnomo
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Quasi Maximum Likelihood

Maximum-Likelihood Event Parameter Estimation from Digital Waveform Capture

2017 ◽  
Author(s):  
Neil C. Momsen ◽  
Garrett Richards ◽  
Matthew A. Kupinski ◽  
Harrison H. Barrett ◽  
Lars R. Furenlid
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood

scholarly journals Technical Note: Approximate Bayesian parameterization of a complex tropical forest model

2013 ◽  
Vol 10 (8) ◽  
pp. 13097-13128 ◽  
Author(s):  
F. Hartig ◽  
C. Dislich ◽  
T. Wiegand ◽  
A. Huth
Keyword(s):  
Parameter Estimation ◽  
Tropical Forest ◽  
Field Data ◽  
Complex Data ◽  
Forest Model ◽  
Likelihood Approximation ◽  
Simulation Based ◽  
Model Predictions ◽  
Approximate Bayesian ◽  
Process Based Models

Abstract. Inverse parameter estimation of process-based models is a long-standing problem in ecology and evolution. A key problem of inverse parameter estimation is to define a metric that quantifies how well model predictions fit to the data. Such a metric can be expressed by general cost or objective functions, but statistical inversion approaches are based on a particular metric, the probability of observing the data given the model, known as the likelihood. Deriving likelihoods for dynamic models requires making assumptions about the probability for observations to deviate from mean model predictions. For technical reasons, these assumptions are usually derived without explicit consideration of the processes in the simulation. Only in recent years have new methods become available that allow generating likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional MCMC, performs well in retrieving known parameter values from virtual field data generated by the forest model. We analyze the results of the parameter estimation, examine the sensitivity towards the choice and aggregation of model outputs and observed data (summary statistics), and show results from using this method to fit the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss differences of this approach to Approximate Bayesian Computing (ABC), another commonly used method to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation, can successfully be applied to process-based models of high complexity. The methodology is particularly suited to heterogeneous and complex data structures and can easily be adjusted to other model types, including most stochastic population and individual-based models. Our study therefore provides a blueprint for a fairly general approach to parameter estimation of stochastic process-based models in ecology and evolution.

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