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demonstrates the distribution of probability over emissions as a function of the size of production, with true and false subsidies, respectively. The last panel in shows the P(Emission | Manufacturing) distribution, obtained by summarizing the two subsidy cases. Note that the slope is negative in each instance, because emissions decrease as supply increases. (The assumption of linearity, of course, implies that at s ome point the emission becomes negative; the linear model is reasonable only if the production size is limited to a narrow range.) The last panel in Figure 1 shows the P(c | h) distribution, averaging over the two possible subsidy values and the two possible subsidy values.

demonstrates the distribution of probability over emissions as a function of the size of production, with true and false subsidies, respectively. The last panel in shows the P(Emission | Manufacturing) distribution, obtained by summarizing the two subsidy cases. Note that the slope is negative in each instance, because emissions decrease as supply increases. (The assumption of linearity, of course, implies that at s ome point the emission becomes negative; the linear model is reasonable only if the production size is limited to a narrow range.) The last panel in Figure 1 shows the P(c | h) distribution, averaging over the two possible subsidy values and the two possible subsidy values.

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A network is a hybrid Bayesian network if it has both discrete and continuous variables. In this research, we discuss how the hybrid Bayesian network can utilized to further understand the network from subsidies, manufacturing to the environmental quality in the context of Hybrid electric vehicles.