Fully probabilistic design of hierarchical Bayesian models

Fully probabilistic design of hierarchical Bayesian models

The minimum cross-entropy principle is an established technique for design of an unknown distribution, processing linear functional constraints on the distribution. More generally, fully probabilistic design (FPD) chooses the distribution—within the knowledgeconstrained set of possible distributions—for which the Kullback-Leibler divergence to the designer’s ideal distribution is minimized. The...

Bayesian Maps: probabilistic and hierarchical models for mobile robot navigation

ion operator and example Having defined the Bayesian Map concept, we now turn to defining operators for combining Bayesian Maps. The one we present here is called the abstraction of maps. It is defined in Fig. 4 and discussed in the rest of this section. As stressed above, in a Bayesian Map, the semantics of the location variable can be very diverse. The main idea behind the abstraction operato...

Fully probabilistic control design

7 Stochastic control design chooses the controller that makes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design describes both the closed loop and its desired behavior in probabilistic terms and uses Kullback–Leibler divergence as 9 their proximity measure. This approach: (i) unifies stochastic control design methodology; (ii) provides explicit min...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models

Analysis of Hierarchical Bayesian Models for Large Space Time Data of the Housing Prices in Tehran

Housing price data is correlated to their location in different neighborhoods and their correlation is type of spatial (location). The price of housing is varius in different months, so they also have a time correlation. Spatio-temporal models are used to analyze this type of the data. An important purpose of reviewing this type of the data is to fit a suitable model for the spatial-temporal an...