The temperature of indoor spaces is at the core of highly relevant topics such as comfort, productivity and health. In conditioned spaces, this temperature is determined by thermostat preferences, but there is a lack of understanding of this phenomenon as a time-dependent magnitude. In addition to this, there is scientific evidence that the mental models of how users understand the operation of the billions of air-conditioning machines around the world are incorrect, which causes systems to ‘compensate’ for temperatures outside by adjusting the thermostat, which leads to erratic changes on set-points over the day. This paper presents the first model of set-point temperature as a time-dependent variable. Additionally, a new mathematical algorithm was developed to complement these models and make possible their identification on the go, called the life Bayesian inference of transition matrices. Data from a total of 75 + 35 real thermostats in two buildings for more than a year were used to validate the model. The method was shown to be highly accurate, fast, and computationally trivial in terms of time and memory, representing a change in the paradigm for smart thermostats.