PRIOR KNOWLEDGE AND POSTERIOR INSIGHTS: APPLYING BAYESIAN METHODS TO MONITORING AND EVALUATION

his paper explores the theoretical foundations, methodological frameworks, software ecosystem, and practical applications of Bayesian inference in evaluation. By explicitly incorporating prior knowledge, modeling uncertainty, and quantifying probabilities of achieving policy thresholds, Bayesian approaches provide evaluators with decision-relevant insights that align with adaptive management cycles in international development, humanitarian response, education, and climate adaptation. The paper demonstrates how Bayesian methods enhance transparency, robustness, and inclusivity while addressing the limitations of frequentist inference. Drawing on examples from social and behavioural change (SBC), gender-based violence (GBV) prevention, peacebuilding, and climate resilience, the analysis illustrates how Bayesian inference can transform evaluation from retrospective judgment into real-time learning. Policy recommendations highlight the need for investments in methodological literacy, open-source software, and participatory approaches to prior elicitation. The conclusion argues that Bayesian evaluation represents not merely a technical innovation but a cultural shift toward embracing uncertainty as the condition of evidence-informed governance.