Parametriestimoinnissa
Parametriestimoinnissa, also known as parameter estimation, is a fundamental concept in statistics and machine learning. It refers to the process of determining the values of parameters in a statistical model that best fit a given dataset. These parameters are typically unknown and must be estimated from the data.
There are several methods for parameter estimation, each with its own advantages and limitations. The most
1. Maximum Likelihood Estimation (MLE): This method involves finding the parameter values that maximize the likelihood
2. Method of Moments: This method uses sample moments (such as mean and variance) to estimate population
3. Bayesian Estimation: This approach incorporates prior knowledge about the parameters into the estimation process. It
4. Least Squares Estimation: Commonly used in linear regression, this method aims to minimize the sum of
Parameter estimation is crucial in various fields, including economics, biology, engineering, and social sciences. It allows