Definitions

1. Parameter estimation is done numerically using the R function optim with box-constraints, i.e.
2. Parameter estimation is performed by using Markov chain Monte Carlo methods from which credible intervals for our predictions are obtained. (Web site)
3. Beyond parameter estimation, the likelihood framework allows us to make tests of parameter values. (Web site)

Mathematics

1. The mathematics of statistical machine translation: Parameter estimation. (Web site)
2. The mathematics of statistical machine translation: parameter estimation. (Web site)

Influence

1. The parameter estimation problem for stochastic differential equations including the influence of measurement error.

Application

1. Its application for parameter estimation requires the computation of error sensitivities by solving an additional ’outer’ dual problem. (Web site)

Theta

1. The choice of theta = 0 provides the model with a flat (uninformative) prior hypothesis, which has the least influence on the parameter estimation.

Regression

1. The tutorial provides an introduction to the ideas of robust statistics including parameter estimation, regression and testing. (Web site)

Outliers

1. It also refers to parameter estimation that can handle outliers. (Web site)

Data Points

1. The second error is the standard error, which, unlike the bias, can be controlled by the number of data points used in parameter estimation. (Web site)

Data

1. Probability theory and its use in physical modeling is covered, as is the statistical analysis of data and parameter estimation.

Statistical Method

1. This technical report describes the statistical method of expectation maximization (EM) for parameter estimation. (Web site)

Calibration

1. For example, the fitting of models to data, called calibration, history matching, and data assimilation, are all synonymous with parameter estimation. (Web site)
2. Calibration: The process of parameter estimation for a model.

Em Algorithm

1. These applets and activities show empirically the properties of the EM algorithm for parameter estimation in diverse settings. (Web site)

Statistical Inference

1. Recently, attention in machine learning and statistical inference has turned to parameter estimation in these models.

Inference

1. Numerous procedures have been developed for parameter estimation and inference in linear regression.

Maximum Likelihood

1. The idea behind maximum likelihood parameter estimation is to determine the parameters that maximize the probability likelihood of - Maximum Likelihood.
2. The above considerations motivate parameter estimation by maximum likelihood in a generalized linear spatial model.

Model Selection

1. It can be used for model selection or for parameter estimation when there are many parameters. (Web site)

Parameter Estimation

1. The two complementary components of inference are parameter estimation (either point or interval estimation) and hypothesis testing. (Web site)
2. We obtain maximum likelihood estimates for all of the parameters and compare the results to standard probabilistic methods of parameter estimation. (Web site)
3. Wu Q.-G., Some results on parameter estimation in extended growth curve models, Journal of Statistical Planning and Inference, 88, 285-300, 2000. (Web site)

Categories

1. Model Selection
2. Encyclopedia of Keywords > Information > Data > Maximum Likelihood
3. Evaluation > Analysis > Tests > Hypothesis Testing
4. Thought > Philosophy > Logic > Inference
5. Communication > Translation > Translation Memory > Parallel Texts
6. Books about "Parameter Estimation" in Amazon.com
   

Continue: More Keywords - - - - - - - - - -