Exact and approximate methods of sampling from posterior distributions of parameters in non-linear ordinary differential equations

This thesis has addressed some of the limitations of current methods that are used to estimate parameters of ODEs and developed novel and efficient methods in this context. These methods facilitate inference by tackling some of the challenges that appear when conducting estimation for ODEs, such as computational time, multimodality and quantification of the uncertainty in parameter values.