As a classical book on subjective probability, de Finetti's ``Theory of probability'' is a must. I found Ref.  particularly stimulating and Ref.  very convincing (the latter represents, in my opinion, the only real introductory, calculus-based, textbook on subjective probability and Bayesian statistics available so far, with many examples and exercises). Unfortunately these two books are only available in Italian at the moment. For Italian readers, I also recommend Refs.  and .
I have consulted Refs.  and , which also contain many references. References , , , ,  , , ,  and  are well-known books among Bayesian. Some literature on Bayesian Networks can be found in Ref. , which also contains interesting URLs. Reference  is a recent Bayesian book close to the physicist's point of view. For developments on Bayesian theory and practical applications I recommend consulting the proceedings of ``Valencia Meetings''  and ``Maxent Workshops'' . An overview of maximum-entropy methods can also be found in Ref. . This last reference and Ref.  show some applications of Bayesian reasoning in statistical mechanics. Other information on Bayesian literature methods can be found on web sites. As a starting point I would recommend Ref. , as well as other sites dedicated to Bayesian networks and artificial intelligence. When integrals become complicated, the Markov Chain Monte Carlo (MCMC) technique becomes crucial: introductions and applications can be found, for example, in Refs.  and .
The applied part of these notes, as well as the critical part, is mostly original. References are given at the appropriate place in the text -- only those actually used have been indicated. Reference  contains applications of some of the methods described here in analyses of HEP data. A concise critical overview of Bayesian reasoning versus frequentistic methods in HEP can be found in Ref. , whilst Ref.  is recommended to those who are still anxious about priors.
As far as measurement uncertainty is concerned, consultation of the ISO Guide is advised. At present the BIPM recommendations are also followed by the American National Institute of Standards and Technology (NIST), whose guidelines are also on the web.