 Nonlinear regression

Nonlinear regression fits a conditional mean whose dependence on its parameters is nonlinear. In a normal distribution observation model, the likelihood is based on residuals $y_i-m(x_i,\theta )$. Nonlinearity can create parameter trade-offs, asymmetric uncertainty and several posterior regions, so prior predictive checks and Markov chain Monte Carlo convergence diagnostics can be especially useful.

Nonlinear regression is a type of regression analysis in which the relationship between the independent variable(s) and the dependent variable is modeled as a nonlinear function. Unlike linear regression, which assumes a straight-line relationship (a linear equation) between the variables, nonlinear regression allows for more complex relationships, accommodating curves and other non-linear shapes.