The priori information, the posterior information and the likelihood in

bayesian probability theory are represented by probability distributions.

As cyberthreat analysts, experts rely on the company's machine learning and

Bayesian probability theory to protect customers from cyberthreats.

Although

Bayesian probability theory offers a coherent and rational approach for source reconstruction, its application to real-world problems using real sensor networks and operational dispersion models will require a better understanding of both the scale and structure of the model error in the predicted concentrations.

While possibility theory and the associated fuzzy set theory are of interest to the control world, the process described in this paper is more suited to the framework of

Bayesian probability theory. It is important to note that Bayesian probability looks at probability as a likelihood to occur and differs from the frequentists' notion of probability theory, which frames probability as a frequency of occurrence.

Bayesian probability theory requires us to make our best guess about the future and then continually revise it as we get new information.

Markov and

Bayesian probability theory are used to infer sink node's trust value and mathematics models are designed in BPTrust.

He is applying a statistical method known as

Bayesian probability theory to translate the calculations that children make during learning tasks into computational models.

After explaining the basic principles of

Bayesian probability theory, the book illustrates their use with a range of examples.