Abstract
Probabilistic Bayesian methods enable combination of information from various sources. The Bayes theorem is explained and its use is illustrated on several examples of practical importance, such as revealing the cause of an accident or reliability increasing of non-destructive testing. Also its use for continuous quantities and for increasing the reliability of the parameters of normal or Weibull distribution is shown.
Keywords
- Statistics
- probability
- Bayes
- Bayes theorem
- reliability
- non-destructive testing
- normal distribution
- Weibull distribution
- combination of information
The term “Bayesian methods” denotes probabilistic methods that enable the combination of information on some event or quantity with previous information from measurement or experience. The use of additional information can increase the reliability of our information or reduce the extent of measurements needed for making conclusions on certain event. Examples of application are the determination of the most probable cause of a failure, increasing the reliability of diagnostic methods or increasing the accuracy of the determination of distribution parameters of random quantities.
Bayesian methods are based on the so-called Bayes theorem [1 – 6]. It was originally formulated for discrete quantities, but extended later for continuous quantities as well. These methods have also been included into standards. In this chapter, their principle will be explained, and the use is shown on several practical examples.
1. Bayes theorem
Let us assume that an event (
where
the summation is done for all possible cases
where the total probability
The materials for road building are delivered from two plants with daily capacities 300 t (plant 1) and 700 t (plant 2). The long-term monitoring of quality shows that plant 1 has 2% of all batches faulty and plant 2 has 4% faulty batches. If now a sample is chosen at random at the building site, and if this sample is faulty, which plant is the batch from?
From the total amount of 300 + 700 = 1000 t/day, plants 1 and 2 produce 30% and 70%, respectively. Let us denote event
Using Bayes rule (3), one can express the probability that the defective specimen is from plant 1 as
If the quality is not considered, the probability that a randomly chosen sample comes from plant 1 equals 30% (i.e. the fraction of production from plant 1). If, however, additional information ”the sample was defective“ was used together with the information on quality in both plants, this probability has dropped to 17.6%. The same information has increased the probability of the sample being from plant 2 from 70% to 82.4%. Although the probability that a sample is from plant 2 was higher even without the Bayes rule (70%), the strengthening of this hypothesis is obvious.
The hypothesis ”the material is from plant 1 (or 2)“ can be strengthened (or mitigated) by checking more specimens. If
This example is adapted from [2]. An explosion occurred during a repair of a tank for liquid natural gas. The accident could have happened due to (1) static electricity, (2) fault in the electric equipment, (3) work with open flame during the repair, or (4) intentional act (sabotage). Engineers for risk analysis estimated that the accident could happen with a probability of 25% due to static electricity, 20% due to a fault in the electric equipment, 40% due to work with open flame, and 75% due to a sabotage. The discussion with them also gave the following subjective assessment of probability of individual causes: 0.30, 0.40, 0.15, and 0.15. What is the most probable cause of the explosion in view of all this information?
Solution. Event
Welded components are tested for the occurrence of defects (cracks). The device used for nondestructive testing is not perfect. It classifies defect correctly (as defect) only with probability 98%, whereas, in 2% of all cases, it does not recognize the crack and classifies the component as good. On the contrary, the device marks 96% of good parts as good, but 4% classifies as with a crack. According to long-term inspection records, 3% of all tested components contain cracks. The questions are: If the tested part was classified as ”wrong“ (i.e. with a defect), what is the probability that it is actually (a) wrong or (b) good? And what about if the component was classified as ”good“?
Solution. Event
Case 1a. Probability that the component marked as wrong is actually wrong, is
Event
Recommendation: All rejected components could be tested once more to reduce the number of discarded good components.
A similar approach can be used in medicine (e.g. in cancer screening).
2. Bayes rule for continuous quantities
If the probability of event
the integration is performed over the whole domain
Now, a question can be asked: If event
where
2. Other applications
Bayesian methods can also be used for the improvement of parameter estimate of various probability distributions. Three examples follow.
The mean value
where
The estimate can be made more accurate if additional information is available (e.g. estimates of
where
Then, the updated confidence interval for
based on the idea that
The ISO 12491 standard ”Statistical methods for quality control of building materials and components“ recommends the following formula for the Bayesian estimate of
where
Some quantities, such as the strength or time to failure due to fatigue, can often be approximated by Weibull distribution:
The parameters
4. Software for Bayesian methods
The problems from the above first three examples can be solved easily using Excel and standard Bayesian notation. Some simple programs can be found in the literature, for example [2, 3]. At ETH Zürich, a PC program Combinfo was created, which enables the combination of data from various sources [8], including vague information, such as probability estimates by experts or by judgment. The program allows assigning various weights to individual information. Bayesian methods are also incorporated into software packages for reliability analysis, such as www.reliability.com, www.weibull.com, www.reliasoft.com, or www.itemsoft.com.
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