Which statement about marginalization in Bayesian inference is true?

Marginalization in Bayesian inference is a technique used to simplify complex probabilistic models by integrating out certain variables, allowing researchers to focus on relevant variables of interest. This is particularly useful in a Bayesian context where one may need to account for uncertainty related to some parameters while concentrating on others that are of primary concern.

The process of marginalization helps derive the distribution of the variable of interest by considering all possible values of the other variables in the model. This is essential in Bayesian inference, where the goal is often to make inferences about specific parameters while acknowledging the uncertainty introduced by other parameters. Thus, option C highlights the core purpose of marginalization in simplifying the analysis and enabling focused reasoning about the variables that matter most for the research question.

The other statements do not accurately reflect the principles of marginalization in Bayesian inference. Marginalization can be performed with both continuous and discrete random variables, it involves not only prior distributions but also likelihoods and other components of the model, and it does not require parameters in a Bayesian model to be fixed.