The system architecture and principle of a remote query answering expert system(RQAES) based on Bayesian reasoning was introduced. The Bayesian method of reasoning and decision making was analyzed, and the design of a knowledge base was presented.

The system architecture and principle of a remote query answering expert system(RQAES) based on Bayesian reasoning was introduced. The Bayesian method of reasoning and decision making was analyzed, and the design of a knowledge base was presented.

After categorizing all the security related system properties into four models, which are represented by their corresponding fuzzy membership functions, the real-time probabilities of whether the specific security properties are abnormal or not will be calculated as according to the fuzzy membership functions of the models they belong to, and a decision whether the supervised system is in a abnormal state is thus made from the synthesizing of all these probabilities by the Bayesian reasoning network.

In this paper, two experiments were designed to examine the Bayesian reasoning's performance features and effective factors of high school students who were in Grade One.

We then report two experiments designed to test whether individuals follow Bayesian reasoning and whether groups are able to overcome biased individual information processing.

After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms.

I re-examine Coherence Arguments (Dutch Book Arguments, No Arbitrage Arguments) for diachronic constraints on Bayesian reasoning.

On the contrary, Bayesian reasoning presupposes knowledge that cannot itself be obtained by Bayesian reasoning, making generic Bayesianism an incoherent theory of belief updating.

It will take into account also examples of mistakes in Bayesian reasoning and mistakes of reasoning committed by clinicians regard inductive reasoning.

A Bayesian Gaussian neural network is put forward for nonlinear system identification. This network has the following features: the topology, connection weights and thresholds can be set immediately when training samples are attainable; the output is a Bayesian reasoning process with the fusion of multi pieces of information; while the training process is only to optimize the input factors, which makes the training simpler than that of back propagation neural network. The self organizing ability...

A Bayesian Gaussian neural network is put forward for nonlinear system identification. This network has the following features: the topology, connection weights and thresholds can be set immediately when training samples are attainable; the output is a Bayesian reasoning process with the fusion of multi pieces of information; while the training process is only to optimize the input factors, which makes the training simpler than that of back propagation neural network. The self organizing ability of this network can also be easily achieved in an optimal way, so that when the dynamics of the system drifts, the network is able to swiftly optimize its connection weights and thresholds according to new training samples, which is very important for on line adaptation of network to systems. In applications, the working of this network can be an alternating process. Simulation shows that this network is comparable to the back propagation neural network in the prediction of a single input single output nonlinear system, and its self organizing ability outperforms the latter which has fixed connection weights and thresholds.

The developments and problems of evidence reasoning are discussed in this article. Evidence reasoning is com-pared with Bayesian reasoning,fuzzy logic,rule-based reasoning and random set theory. Improvement of evidence reasoning andits extension to probability and fuzzy set are introduced It is found that evidence theory has many applications Finally, its direc-tion of further development is proposed.

The influence diagram is introduced to maneuvering decision making in one-to-one air combat for the first time. In the influence diagram model, the possible combat situations related to each maneuver alternative are associated with a probability and a utility. The effects of sensor information that will reduce the uncertainty of the model are evaluated with Bayesian reasoning. Influence diagram analysis produces a probability distribution of the overall utility that represents the successfulness of a maneuver....

The influence diagram is introduced to maneuvering decision making in one-to-one air combat for the first time. In the influence diagram model, the possible combat situations related to each maneuver alternative are associated with a probability and a utility. The effects of sensor information that will reduce the uncertainty of the model are evaluated with Bayesian reasoning. Influence diagram analysis produces a probability distribution of the overall utility that represents the successfulness of a maneuver. The final decision is based on the decision criterion. At last, the impacts of different factors on the outcome of the maneuvering decision are determined by sensitivity analysis.