This chapter provides a comprehensive introduction to Bayesian modelling, with a focus on its application to wildlife monitoring and ecological inference. It outlines the advantages of Bayesian hierarchical models for managing uncertainty, incorporating prior knowledge and integrating diverse data sources. It then offers practical guidance on implementing Bayesian models, diagnosing convergence, handling missing data, and choosing appropriate software tools. Advanced topics in spatial and temporal modelling are explored, culminating in a case study that uses hierarchical Poisson regression to examine how prey species influence red fox abundance across Japanese landscapes. The chapter aims to equip ecologists and applied statisticians with the tools and understanding needed to apply Bayesian methods in conservation and wildlife research.