DOI link for Parameter Estimation
Parameter Estimation book
In the previous chapter we introduced a rigorous framework to stochastically describe the data that is acquired in imaging experiments. We therefore have the necessary background to carefully analyze questions of quantitation. As the data we obtain is stochastic in nature, we are confronted with statistical parameter estimation problems. It is the discussion of approaches to parameter estimation, such as maximum likelihood estimation, that will be the main focus of this chapter. In addition to a general introduction to maximum likelihood estimation, log-likelihood functions for the image data models described in the prior chapter will be presented. For evaluating the quality of an estimator, the reader will also be introduced to two important criteria, namely the bias and the variance of an estimator.