Parametrlar

Parametrlar, or parameters, are quantities that define a system, model, or function and are typically treated as constants within a given context. They determine the behavior or shape of a relation but are not the data being studied themselves. In mathematics and physics, parameters set the conditions of an equation; for example, in the linear function y = a x + b the values of a and b are parameters that shape the slope and intercept, while x and y are variables that can take on different values.

In statistics, parameters refer to fixed, unknown quantities that describe a population, such as the mean μ or the standard deviation σ of a distribution. They contrast with sample statistics, which are computed from data. Parameter estimation seeks to infer the values of these population parameters from observed data, using methods such as maximum likelihood or Bayesian inference. Some models are parameterized, meaning their form is specified by a set of parameters.

In computer science and machine learning, model parameters are learned from data during training, for example weights and biases in neural networks. Hyperparameters are external settings chosen before training, such as learning rate or regularization strength. The distinction between parameters and hyperparameters is important for model selection and generalization.

Parameterization also supports sensitivity analysis and identifiability studies, helping researchers understand how changes in parameters affect outputs and whether the parameters can be uniquely determined from the data.