# Point estimation

**point estimatepointpoint estimatorestimatedpoint estimates**

In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).wikipedia

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### Estimator

**estimatorsestimateestimates**

More formally, it is the application of a point estimator to the data to obtain a point estimate.

There are point and interval estimators.

### Confidence interval

**confidence intervalsconfidence levelconfidence**

Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

Interval estimation can be contrasted with point estimation.

### Interval estimation

**interval estimateintervalInterval (statistics)**

Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

In statistics, interval estimation is the use of sample data to calculate an interval of possible values of an unknown population parameter; this is in contrast to point estimation, which gives a single value.

### Maximum likelihood estimation

**maximum likelihoodmaximum likelihood estimatormaximum likelihood estimate**

The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate.

### Maximum a posteriori estimation

**maximum a posterioriMAPposterior mode**

The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data.

### Statistics

**statisticalstatistical analysisstatistician**

In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Sample (statistics)

**samplesamplesstatistical sample**

In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Data

**statistical datascientific datadatum**

In statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate since it identifies a point in some parameter space) which is to serve as a "best guess" or "best estimate" of an unknown population parameter (for example, the population mean).

### Point (geometry)

**pointpointslocation**

### Parameter space

**weight space**

### Parameter

**parametersparametricargument**

### Mean

**mean valueaveragepopulation mean**

### Frequentist inference

**frequentistfrequentist statisticsclassical**

Point estimation can be contrasted with interval estimation: such interval estimates are typically either confidence intervals, in the case of frequentist inference, or credible intervals, in the case of Bayesian inference.

### Credible interval

**credible intervalscredible regionBayesian interval**

### Bayesian inference

**BayesianBayesian analysisBayesian method**

### Minimum-variance unbiased estimator

**minimum variance unbiased estimatorbest unbiased estimatorUMVU**

### Loss function

**objective functioncost functionrisk function**

### Gauss–Markov theorem

**best linear unbiased estimatorGauss–Markovbest linear unbiased estimation**

### Median

**averagesample medianmedian-unbiased estimator**

### Method of moments (statistics)

**method of momentsmethod of matching momentsmethod of moment matching**

### Posterior probability

**posterior distributionposteriorposterior probability distribution**

The Minimum Message Length (MML) point estimator is based in Bayesian information theory and is not so directly related to the posterior distribution.

### Central tendency

**LocalityLocality (statistics)Measure of central tendency**

### Admissible decision rule

**admissibleadmissibilityinadmissible**

### Minimum message length

**MMLmessage length**

The Minimum Message Length (MML) point estimator is based in Bayesian information theory and is not so directly related to the posterior distribution.