结合贝叶斯网络和神经网络，提出了一种建立数据驱动型的动态线性回归系统模型的方法。
A new method was represented to model dynamic linear regression system driven by data, in which a bayesian network was combined with the RBF neural network.
最常使用的五个模型是石油期货价格、回归结构模型、时间序列分析、贝叶斯自回归模型和动态随机一般均衡图。
The five models used most often are oil futures prices, regression-based structural models, time-series analysis, Bayesian autoregressive models and dynamic stochastic general equilibrium graphs.
讨论了加速失效模型族中最简单而又十分重要的指数回归模型，利用贝叶斯方法提高了该模型的有效性。
This paper discusses the exponential regression model, which is the simplest but very important in the family of Bayesian accelerated failure-time model.
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