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I'm running a Bayesian model and I'm stuck on some aspect of the model that I have difficulty to understand. Since my knowledge about Bayesian inference is limited, I would like to have some good references to understand Bayesian statistics.

People are teaching Bayesian statistics could be interested in how to think in bayesian versus frequentist statistics. But I'm interested in finding examples of model building and how should we create, from scratch new models and diagnose them.

A good reference is this book, but I need something more practical.

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The book you linked (Gelman et al.) is eminently practical: the authors spend quite a few pages discussing real data and the gory details of choosing, fitting, and evaluating models. That's where I learned Bayesian stuff myself. If what you're looking for is something less mathematically intimidating, try John K. Kruschke's Doing Bayesian Data Analysis.

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