用博弈论求解该模型，得到了完美贝叶斯均衡解，进而给出了产险公司在谈判中能获得的最大期望收益与投保大户的最优策略。
Then the perfect Bayesian equilibrium of the game model in the form of a proposition is given, and the proposition is proved in detail by backward induction.
研究表明，博弈模型存在某种形式的精炼贝叶斯纳什均衡，而且道德风险的存在会使保险单价格上升。
The research shows that the game model has a perfect Bayesian Nash equilibrium and the existing of moral haz.
初步的研究表明这个信号博弈存在着分离均衡（贝叶斯精练均衡的一种），这意味着在不完全信息的情况下保险合同是可以达成的。
The primary study reveals that it exists separate equilibrium (one of Bayesian refinement equilibria), which means in the case of incomplete information insurance contract can be reached.
研究表明，博弈模型存在某种形式的精炼贝叶斯纳什均衡，但如实告知一般不是保单持有人的精炼贝叶斯纳什均衡策略。
The research shows that the game model has a perfect Bayesian Nash equilibrium, whereas telling the truth is not always a perfect Bayesian Nash equilibrium of the policyholder.
研究表明，博弈模型存在某种形式的精炼贝叶斯纳什均衡，而且道德风险的存在会使保险单价格上升。
The research shows that the game model has a perfect Bayesian Nash equilibrium and the existing of moral hazard elevates the price of insurance policy.
采用线性战略组合，解出了该贝叶斯博弈的均衡解，并且定义了配电商的成本和电能消费者的收益。
This paper interprets the balanced results of Bayes game with linear strategy, and gives out the definitions of electricity utility cost and electricity customers profit.
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.
通过混合策略的贝叶斯纳什均衡给出第三方测试过程适用条件，采用沙普利值对参与方合作可能性进行探讨。
The applicability of third-part test process is illustrated as Bayesian Nash Equilibrium of the compound strategies game model, and a discussion on cooperation possibility is made with Shapley value.
课程还讨论了一些经济学现象和概念，例如均衡、理性选择、收益最大化、贝叶斯信念、赛局理论、不确定条件下行为等。
Economic concepts such as equilibrium, rational choice, utility maximization, Bayesian beliefs, game theory, and behavior under uncertainty are discussed in light of these phenomena.
我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
我们将稀疏贝叶斯学习与序列蒙特卡罗盲均衡算法结合，提高了原算法的性能。
We integrate the Sparse Bayesian Learning algorithm into the SMC blind receiver to improve the performance under sparse channels.
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