💡 Words with a Similar Meaning to "Backfitting algorithm"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| optimal estimation | In applied statistics, optimal estimation is a regularized matrix inverse method based on Bayes' theorem. |
| estimating equations | In statistics, the method of estimating equations is a way of specifying how the parameters of a statistical model should be estimated. |
| stepwise regression | In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. |
| ordinary least squares | In statistics, ordinary least squares is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values of the variable being observed) in the input dataset and the output of the (linear) function of the independent variable. |
| nested sampling algorithm | The nested sampling algorithm is a computational approach to the Bayesian statistics problems of comparing models and generating samples from posterior distributions. |
| best linear unbiased prediction | In statistics, best linear unbiased prediction is used in linear mixed models for the estimation of random effects. |
| inverse iteration | In numerical analysis, inverse iteration (also known as the inverse power method) is an iterative eigenvalue algorithm. |
| poisson regression | In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. |
| generalized linear model | In statistics, a generalized linear model is a flexible generalization of ordinary linear regression. |
| maximum likelihood estimation | In statistics, maximum likelihood estimation is a method of estimating the parameters of an assumed probability distribution, given some observed data. |
| regressionnoun | (statistics) An analytic method to measure the association of one or more independent variables with a dependent variable. |
| simple linear regression | In statistics, simple linear regression is a linear regression model with a single explanatory variable. |
| probability distribution fitting | Probability distribution fitting or simply distribution fitting is the fitting of a probability distribution to a series of data concerning the repeated measurement of a variable phenomenon. |
| rejection samplingnoun | (computational statistics, numerical analysis) A technique used to generate observations from a distribution by repeatedly sampling an approximation function that bounds the distribution everywhere from above and rejecting the samples based on the quotient of the two functions at the sample's location. |
| additive model | In statistics, an additive model is a nonparametric regression method. |
| generalized method of moments | In econometrics and statistics, the generalized method of moments is a generic method for estimating parameters in statistical models. |
| bayesian vector autoregression | In statistics and econometrics, Bayesian vector autoregression uses Bayesian methods to estimate a vector autoregression model. |
| residual sum of squaresnoun | (statistics) The sum of the squared residuals between some model and a data set; a measure of how well the model fits with the data. |
| likelihood function | A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. |
| backpropagationnoun | (machine learning) An error correction technique used in neural networks. |
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