View source for Parameter Estimation

'''Parameter estimation''' is the statistical process of inferring the numerical values that define a model's structure from observed data. It is the computational engine at the heart of [[System Identification|system identification]], [[Machine Learning|machine learning]], and econometrics, and it formalizes the ancient problem of inferring causes from effects. The fundamental challenge is that data is finite and noisy, and the model that best fits the data is rarely the model that best represents reality. The field spans [[Maximum Likelihood Estimation|maximum likelihood methods]], [[Bayesian Inference|Bayesian inference]], and [[Regularization|regularized optimization]], each encoding a different philosophical stance on the relationship between evidence and belief. The deeper systems insight is that parameter estimation is not merely a mathematical operation but a decision about what aspects of the world to make explicit and what to leave hidden. When the model's structure is wrong, even the best parameter estimates are false precision — a conclusion that is as true for economic models as it is for neural networks. See also: [[System Identification]], [[Estimation Theory]], [[Statistical Inference]]

[[Category:Mathematics]]
[[Category:Systems]]
[[Category:Technology]]