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This predictive distribution is simply the marginal distribution on n after integrating out uncertainty on Similar results obtain for the case when more than one uncertain parameter is involved, that is for vector Note that the predictive distribution combines uncertainty due to natural variability, that is, the number of occurrences n given the parameter , with uncertainty due to limited knowledge, that is, the uncertainty in the model parameter itself If more than one aspect of natural variability depends on the same uncertain parameter (eg number of occurrences in two separate periods, n1 and n2 ) then forming the predictive distribution of each in isolation from the other will mask the implied correlation caused by their shared dependence on the same uncertain realization of This is one of the arguments for separating out natural variability from limited knowledge using logic trees.

Listing 6.8 (continued)

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00401D59 00401D5C 00401D5E 00401D64 00401D6A 00401D70 00401D76 00401D7A 00401D7B 00401D7C 00401D7E 00401D82 00401D85 00401D88 00401D8D 00401D8F 00401D92 00401D95 00401D9A 00401D9C 00401DA1 00401DA3 00401DA4 00401DA8 00401DAD 00401DAE 00401DB4 00401DB6 00401DBC 00401DBF 00401DC4 00401DC6 00401DCB 00401DCF 00401DD1 00401DD5 00401DD6 00401DD7 00401DDC 00401DDD 00401DE3 00401DE6 00401DE8 00401DEF 00401DF5 00401DF6 00401DF7

The usual approach to quantifying the probability distribution for an uncertain model parameter, such as , based on statistical data is to start from some uniform or noninformative probability density function (pdf) on the parameter, presumed to exist prior to having observed the data, and then to update that probably distribution using Bayes Theorem, as reviewed in 4 Modeling the occurrence of initiating events as a Poisson process typically presumes the uncertainty in those events to be due to natural variability in time or space Given the average rate of occurrence speci ed by the parameter , the actual occurrence of initiating events is a purely stochastic process The only place that knowledge uncertainty enters the formulation is in the statistical estimate of the parameter itself (which may be far from negligible) The assumption that initiating events are stochastic in time or space is convenient but not necessary.

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ADD ESP,14 TEST EDI,EDI JE cryptex.00401E39 MOV ESI,DS:[<&KERNEL32.GetConsoleScreenBufferInfo>] LEA EBX,DS:[EBX] MOV EDX,DS:[40504C] LEA ECX,SS:[ESP+2C] PUSH ECX PUSH EDX CALL ESI FLD DWORD PTR SS:[ESP+10] SUB ESP,8 FSTP QWORD PTR SS:[ESP] PUSH cryptex.00403320 ; ASCII %2.2f percent completed. CALL EBP ADD ESP,0C CMP EDI,1 MOV EAX,0FFC JA SHORT cryptex.00401DA1 MOV EAX,DS:[405050] PUSH 0 PUSH EAX MOV EAX,SS:[ESP+24] PUSH cryptex.00405054 PUSH EAX CALL DS:[<&ADVAPI32.CryptHashData>] TEST EAX,EAX JE cryptex.00401EEE CMP EDI,1 MOV EAX,0FFC JA SHORT cryptex.00401DCB MOV EAX,DS:[405050] MOV EDX,SS:[ESP+14] PUSH 0 ; /pOverlapped = NULL LEA ECX,SS:[ESP+2C] ; | PUSH ECX ; |pBytesWritten PUSH EAX ; |nBytesToWrite PUSH cryptex.00405054 ; |Buffer = cryptex.00405054 PUSH EDX ; |hFile CALL DS:[<&KERNEL32.WriteFile>] SUB EDI,1 JE SHORT cryptex.00401E00 MOV EAX,SS:[ESP+8C] MOV ECX,DS:[405050] PUSH EAX PUSH ECX PUSH EBX

Listing 6.8 (continued)

In the case of extreme storms, one could model storm events based on rst principles of meteorology Given this approach, the occurrence and magnitude of storm events are results of the modeling activity, and are not necessarily treated as random in time or space Uncertainty enters this approach to modeling extreme events in the statistical estimates of the parameters of the meteorological model and in model error This approach trades off the uncertainty due to natural variability for uncertainty due to limited knowledge Usually, an event tree analysis presumes stationarity for stochastic initiating events, but for events such as ood volumes or river stages the assumption may not be good Changing upstream conditions in land use, channelization, and other factors may cause ood frequencies or rating curves to gradually change with time This means that the probability distributions used to represent random variables, such as peak ood discharge, may change..

Reliability models reason from rst principles of mechanics or natural science to calculate uncertainties in the performance of speci ed variables ( 13). For example, such models might start from uncertainties in soil engineering properties in an embankment

00401DF8 00401DFD 00401E00 00401E05 00401E09 00401E0A 00401E0B 00401E0D 00401E11 00401E17 00401E18 00401E19 00401E1F 00401E21 00401E27 00401E2D 00401E33 00401E39 00401E3F 00401E42 00401E45 00401E4A 00401E4C 00401E51 00401E53 00401E55 00401E59 00401E5D 00401E61 00401E66 00401E69 00401E6D 00401E71 00401E75 00401E7A 00401E82 00401E83 00401E87 00401E88 00401E89 00401E8B 00401E8C 00401E92 00401E94 00401E96

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