We present a method to simultaneously infer the interstellar extinction parameters $A_0$ and $R_0$, stellar effective temperature $T_{rm eff}$, and distance modulus $mu$ in a Bayesian framework. Using multi-band photometry from SDSS and UKIDSS, we train a forward model to emulate the colour-change due to physical properties of stars and the interstellar medium for temperatures from 4000 to 9000 K and extinctions from 0 to 5 mag. We introduce a Hertzsprung-Russel diagram prior to account for physical constraints on the distribution of stars in the temperature-absolute magnitude plane. This allows us to infer distances probabilistically. Influences of colour information, priors and model parameters are explored. Residual mean absolute errors (MAEs) on a set of objects for extinction and temperature are 0.2 mag and 300 K, respectively, for $R_0$ fixed to 3.1. For variable $R_0$, we obtain MAEs of 0.37 mag, 412.9 K and 0.74 for $A_0$, $T_{rm eff}$ and $R_0$, respectively. Distance moduli are accurate to approximately 2 mag. Quantifying the precisions of individual parameter estimates with $68%$ confidence interval of the posterior distribution, we obtain 0.05 mag, 66 K, 2 mag and 0.07 for $A_0$, $T_{rm eff}$, $mu$ and $R_0$, respectively, although we find that these underestimate the accuracy of the model. We produce two-dimensional maps in extinction and $R_0$ that are compared to previous work. Furthermore we incorporate the inferred distance information to compute fully probabilistic distance profiles for individual lines of sight. The individual stellar AP estimates, combined with inferred 3D information will make possible many Galactic science and modelling applications. Adapting our method to work with other surveys, such as Pan-STARRS and Gaia, will allow us to probe other regions of the Galaxy.
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