The use of accelerometers in wildlife tracking provides a fine-scale data source for understanding animal behavior and decision making. We modeled transitions between flying, feeding, stationary and walking behavior states using a first-order Bayesian Markov model, introducing Polya-Gamma latent variables for automatic sampling of covariate coefficients from the posterior distribution. Our model provides a unifying framework for including both acceleration and GPS data, with straightforward inference of behavioral time allocation across used habitats.