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Description: This tutorial introduces the reader to Bayesian analysis of ecological momentary assessment (EMA) data as applied in psychological sciences. We discuss practical advantages of the Bayesian approach over frequentist methods as well as conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (1) incorporate prior knowledge and beliefs in analyses, (2) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (3) quantify the uncertainty of effect size estimates, and (4) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, with data and code made available at https://osf.io/rh2sw/. Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.