## 3 Results
### Question 8: How Do We Update the IEQ Models? Despite the many IEQ models that have been developed, hardly any were employed again in later studies because the models, especially in terms of the weighting values of different IEQ factors, varied with the building types, occupants' professions, the purposes of occupation, etc. However, the process will be prolonged and laborious if a new model is needed every time the IEQ in a building is evaluated. Besides, as mentioned in the above section, sometimes it is difficult to collect enough data to develop a model with sufficient accuracy. [PERSON] et al. [96] first proposed a Bayes theorem-based method to update existing models, using limited data to deal with these issues. They established a new relationship between PMV and PPD, contemporizing the PMV model with their newly collected data. Similarly, [PERSON] et al. [97] used the Bayesian approach to update [PERSON]'s PMV-PPD model to better fit the occupants' thermal sensations, as collected in offices, classrooms, apartments, and elderly care centers. In these cases, the PPD calculated using [PERSON]'s model was the prior probability \(P(A)\) used in [PERSON] theorem. The dissatisfaction percentages that were calculated based on the survey data were represented by \(P(B)\) in [PERSON] theorem. According to [PERSON] theorem, the updated dissatisfied percentage (namely, the posterior probability) can be calculated using the following equations:  
\[P(A|B)=\frac{P(B|A)P(A)}{P(B)} \tag{21}\]  
\[P(B|A)=C_{n}^{k}P(A)^{k}(1-P(A))^{n-k} \tag{22}\]  
where \(k\) is the number of dissatisfaction cases and \(n\) is the number of collected instances. Applying the same approach, [PERSON] et al. [98] updated two IEQ models (one was a linear regression model, where four IEQ factors were equally weighted, and the other was a logistic regression model that referred to a previous study), using only 52 cases collected in residential buildings. With the Bayesian approach, the IEQ models developed by previous studies could be used as prior information for future IEQ performance models, offering material closer to the newly collected field data. This way, more accurate IEQ assessment models could be developed using minor amounts of data. Besides the additional data, additional IEQ factors, apart from the four main aspects, were also suggested for inclusion in the IEQ model. It has been found that privacy, office layout, cleaning, and maintenance might influence occupants' satisfaction with IEQ [99]. However, almost none of the existing IEQ models considered the impact of these factors on the overall IEQ performance, which might lead to discrepancies between the predicted and actual IEQ performances. To address this limitation, [PERSON] et al. [11] first proposed an open IEQ acceptance model, which was more inclusive and accurate than 'traditionalIEQ models' based on regression analyses. This open acceptance model adopted frequency distribution functions; the overall IEQ acceptance is calculated using Equation (23):  
\[\Phi=\sum\
olimits_{j=1}^{i^{2}}\varphi_{j}\rho_{j} \tag{23}\]  
where \(\varphi_{j}\) is the occurrence probability of each condition; \(\rho_{j}\) is the acceptance probability with each condition, determined by the collected data; \(i\) is the number of IEQ factors that influence the overall IEQ satisfaction. For each factor, there are two possibilities: acceptable and unacceptable (the acceptance probability with each IEQ factor is \(\delta_{i}\)); therefore, the investigated environments can be classified into \(i^{2}\) conditions. If we take the model with four IEQ factors (\(i\) = 4) as an example, the related information is shown in Table 2.  
This open IEQ model is flexible to allow for the inclusion of additional IEQ factors and is feasible for revising previous models with newly collected data. Moreover, applying this open model is more straightforward because no regression analysis is needed.