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 . 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.
Articles by others on the same topic
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.