Bayesian inference is an important technique throughout statistics. The essence of Beyesian inference is to derive the posterior belief updated from prior belief by the learned information, which is a set of differentially private answers under differential privacy. Although Bayesian inference can be used in a variety of applications, it becomes theoretically hard to solve when the number of di...

Bayesian network is a strong tool for uncertain knowledge representation and inference. This paper mainly introduces some technologies and methods about Bayesian network based on intelligent system. In the construction of Bayesian network, divorcing technology and noisy-or technology are used. In the inference of Bayesian network, VE algorithm and sampling algorithm are introduced. Finally, Bay...

We address the problem of providing inference for parameters selected after viewing the data. A frequentist solution to this problem is False Discovery Rate adjusted inference. We explain the role of selection in controlling the occurrence of false discoveries in Bayesian analysis, and argue that Bayesian inference may also be affected by selection – in particular Bayesian inference based on su...