10.1 Probabilities of One Event
145
Fig. 10.3 Uniform
distribution f parameter x
between its estimated
extreme values Min, Max
f(x)
1/(Max-Min)
X
Min
Max
0
Fig. 10.4 A priori and a
posteriori distribution of a
parameter x between its
estimated extreme values 0.2
and 1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
P'(p=pi)
P''(p=pi)
integrate these with any factual data the client may deliver to us; we adjust the values
to take into account the specific location, habits, and possible future conditions; and
finally, we obtain a framing range of probabilities and consequences for each type
of event and for each identified scenario.
We live in a dynamic world of climate changes and environmental modifications. If any
scenario is not present in an analysis it means that, at that time and with the data at hand,
it was deemed as bordering on credibility, i.e., as having a probability of occurrence
in the order of 10 −6 to 10 −5 (based on various industries’ customary definition of
credibility, as discussed earlier). We firmly discourage limiting risk assessments to
“credible scenarios”, as this common practice leads to bias and censoring the results 2
(Oboni and Oboni 2018). Events that are below credibility should fall out of the
analysis “by themselves”, and not because they are the object of arbitrary decisions.
Bayesian updates can be developed, provided the a priori ranges of probabilities,
consequences, or any parameter of interest are “wide enough”.
Any risk assessment should be updated if the frequencies, intensities, patterns of
the hazards and/or vulnerabilities, or robustness of the systems change over time.
2 Because the credibility threshold may not be properly defined and people tend to instinctively place
it orders of magnitude higher than the technical definition.
145
Fig. 10.3 Uniform
distribution f parameter x
between its estimated
extreme values Min, Max
f(x)
1/(Max-Min)
X
Min
Max
0
Fig. 10.4 A priori and a
posteriori distribution of a
parameter x between its
estimated extreme values 0.2
and 1
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
P'(p=pi)
P''(p=pi)
integrate these with any factual data the client may deliver to us; we adjust the values
to take into account the specific location, habits, and possible future conditions; and
finally, we obtain a framing range of probabilities and consequences for each type
of event and for each identified scenario.
We live in a dynamic world of climate changes and environmental modifications. If any
scenario is not present in an analysis it means that, at that time and with the data at hand,
it was deemed as bordering on credibility, i.e., as having a probability of occurrence
in the order of 10 −6 to 10 −5 (based on various industries’ customary definition of
credibility, as discussed earlier). We firmly discourage limiting risk assessments to
“credible scenarios”, as this common practice leads to bias and censoring the results 2
(Oboni and Oboni 2018). Events that are below credibility should fall out of the
analysis “by themselves”, and not because they are the object of arbitrary decisions.
Bayesian updates can be developed, provided the a priori ranges of probabilities,
consequences, or any parameter of interest are “wide enough”.
Any risk assessment should be updated if the frequencies, intensities, patterns of
the hazards and/or vulnerabilities, or robustness of the systems change over time.
2 Because the credibility threshold may not be properly defined and people tend to instinctively place
it orders of magnitude higher than the technical definition.