Többparaméteres

Többparaméteres is an adjective used in Hungarian to describe something that depends on more than one parameter. It is commonly applied to mathematical models, functions, distributions, or systems where a vector of parameters, rather than a single scalar value, specifies its behavior or characteristics.

In mathematics and statistics, a töbparaméteres modell is described by a parameter vector θ that belongs to a multi-dimensional space. Examples include linear regression with multiple coefficients, generalized linear models, and many time-series or econometric models. Inference for such models often involves estimating several parameters simultaneously, using methods such as maximum likelihood or Bayesian estimation, and assessing uncertainty through confidence intervals or posterior distributions.

A többparaméteres eloszlás (multivariate distribution) is a probability distribution characterized by more than one parameter. Classic examples include the multivariate normal distribution, which is determined by a mean vector μ and a covariance matrix Σ, and other families like the Dirichlet or Wishart distributions. In these cases, the estimation and interpretation of multiple parameters can be more complex due to parameter dependencies and identifiability considerations.

Applications of többparaméteres approaches span many fields, including engineering, physics, finance, and data science. They enable modeling of systems with several influencing factors, calibration and fitting of complex models, and multivariate inference where multiple outcomes or dimensions are analyzed jointly. Challenges often involve numerical optimization, parameter correlation, and model selection when balancing fit and complexity.