paramétervektor

A paramétervektor, often translated as parameter vector, is a fundamental concept in various fields, including mathematics, statistics, and machine learning. It is a vector where each element represents a parameter that defines a specific model or system. These parameters are typically numerical values that can be adjusted to best fit observed data or to achieve a desired outcome.

In essence, a paramétervektor acts as a configuration for a model. For instance, in linear regression, a paramétervektor would contain the coefficients of the independent variables, along with the intercept. Changing these values alters the slope and position of the regression line. Similarly, in machine learning algorithms like neural networks, a paramétervektor encapsulates the weights and biases of the network's connections, which are learned during the training process.

The process of determining the optimal values for a paramétervektor is often referred to as parameter estimation or model fitting. This typically involves an optimization algorithm that seeks to minimize an objective function, such as a loss function or error metric. By iteratively adjusting the elements of the paramétervektor, the algorithm aims to find the set of values that best explains the data or performs a given task most effectively. The dimensionality of the paramétervektor can vary greatly depending on the complexity of the model.