Exam Revision Deck

Question 1

Common Cause Failure
The Poisson process is the simplest process with
• \_\_\_\_\_ Shocks
• \_\_\_\_\_ distributed shocks
• Uses \_\_\_\_\_\_

Answer 1

Common Cause Failure
The Poisson process is the simplest process with
• COMMON Shocks
• EXPONENTIAL distributed shocks
• Uses SUPERPOSITION

Question 2

Common Cause Failure
The Beta Factor process focuses on proportions, with
• _____ Shocks which affect all components
• _____ Shocks
• β is the _______ of Common shock failures

Answer 2

Common Cause Failure
The Beta Factor process focuses on proportions, with
• COMMON Shocks which affect all components
• INDEPENDENT Shocks
• β is the PROPORTION of Common shock failures

Question 3

Common Cause Failure
The Binomial process uses
• \_\_\_\_\_ Shocks (μ)
• \_\_\_\_\_ Shocks (λ)
• \_\_\_\_\_ Failures (p)

Answer 3

Common Cause Failure
The Binomial process uses
• COMMON Shocks (μ)
• INDEPENDENT Shocks (λ)
• INDEPENDENT Failures (p)

Question 4

Common Cause Failure
The Alpha process depends on group size, and has
• Fewer \_\_\_\_\_
• \_\_\_\_\_ Probability (α)
• Group size (\_\_)
• Overall \_\_\_\_ rate (λ)

Answer 4

Common Cause Failure
The Alpha process depends on group size, and has
• Fewer PARAMETERS
• FAILURE Probability (α)
• Group size (n)
• Overall FAILURE rate (λ)

Question 5

Bayesian Belief Networks

Mathematically → ______

Answer 5

Bayesian Belief Networks

Mathematically → CONDITIONAL INDEPENDECE

Question 6

Bayesian Belief Networks

Practically → ______

Answer 6

Bayesian Belief Networks

Practically → PERCEIVED RELATIONSHIP

Question 7

Confidence Intervals (Estimated Variance)
• The T-Distribution is widely used in statistical \_\_\_\_\_
• DF = \_\_\_\_\_
• Mean = \_\_\_\_\_
• Variance = \_\_\_\_\_\_

Answer 7

Confidence Intervals (Estimated Variance)
• The T-Distribution is widely used in statistical INFERENCE
• DF = n-1
• Mean = 0
• Variance = n/(n-2)

Question 8

Confidence Intervals (Estimated Variance)
The χ distribution is a \_\_\_\_\_ distribution, with
• Mean = \_\_\_\_\_
• Variance = \_\_\_\_\_\_

Answer 8

Confidence Intervals (Estimated Variance)
The χ distribution is a GAMMA distribution, with
• Mean = n
• Variance = 2n

Question 9

Confidence Intervals (Central Limit)
• Uses \_\_\_\_\_\_\_ distribution

Answer 9

Confidence Intervals (Central Limit)
• Uses BINOMIAL distribution

Question 10

Confidence Intervals (Poisson)
• Uses \_\_\_\_\_\_\_ DF

Answer 10

Confidence Intervals (Poisson)
• Uses 2(n+1) DF

Question 11

Confidence Intervals (Exponential)
• Uses \_\_\_\_\_\_\_ DF

Answer 11

Confidence Intervals (Exponential)
• Uses 2n DF

Question 12

Thurstone Model (\_\_\_\_\_ Comparison)
• Normally \_\_\_\_\_\_
• Mean = \_\_\_\_\_
• Uses constant expert \_\_\_\_\_ \_\_\_\_\_\_
• Expert assessment is \_\_\_\_\_\_\_
• Uses \_\_\_\_ values for calibration

Answer 12

Thurstone Model (PAIRED Comparison)
• Normally DISTRIBUTED
• Mean = TRUE VALUE
• Uses constant expert STANDARD DEVIATIONS
• Expert assessment is UNCORRELATED
• Uses 2 values for calibration

Question 13

Bradley-Terry Model (_____ Comparison)
• Requires a _____ factor
• Used in customer ____ applications
• Uses ____ values for calibration

Answer 13

Bradley-Terry Model (PAIRED Comparison)
• Requires a SCALING factor
• Used in customer CONFIDENCE applications
• Uses 1 value for calibration

Question 14

Apost-Mosleh Model (Bayesian)
• Each expert answers X = ______
• Mean is a measurement of _____
• Standard Dev is a measure of _____

Answer 14

Apost-Mosleh Model (Bayesian)
• Each expert answers X = x+ε
• Mean is a measurement of BIAS
• Standard Dev is a measure of ACCURACY

Question 15

Cooke's Method (Classical)
• Satisfies 5 requirements (\_\_\_\_\_\_)
• Calibrations reflects \_\_\_\_\_
• Information reflects \_\_\_\_\_
• Global Weight → f(\_\_\_\_\_ information)
• Item Weight → f(\_\_\_\_\_ information)

Answer 15

Cooke's Method (Classical)
• Satisfies 5 requirements (FERNA)
• Calibrations reflects ACCURACY
• Information reflects UNCERTAINTY BOUNDS
• Global Weight → f(AVERAGE information)
• Item Weight → f(ITEM information)

Question 16

Judging Probabilities
Normalised Geometric takes the ______ of each event then _______.
It satisfies the ______ property requirement

Answer 16

Judging Probabilities
Normalised Geometric takes the WEIGHTED MEAN of each event then NORMALISES.
It satisfies the INDEPENDENCE property requirement

Question 17

Judging Probabilities
Arithmetic takes ____ arithmetic probabilities as the weighted mean
It satisfies the ______ property requirement

Answer 17

Judging Probabilities
Arithmetic takes COMBINED arithmetic probabilities as the weighted mean
It satisfies the MARGINALISATION property requirement

Question 18

Monte Carlo
The repeated simulation of ____ _____ quantities.
_____ errors will always remain.

Takes a ______ → through the model → gives ______ on output

Answer 18

Monte Carlo
The repeated simulation of INDEPENDENT RANDOM quantities.
SAMPLING errors will always remain.

Takes a PROBABILITY→ through the model → gives UNCERTAINTY on the outputs

Question 19

Monte Carlo

Uses ______ to generate pseudo random numbers, in addition to _____ reduction.

Answer 19

Monte Carlo

Uses MIXED CONGRUENTIAL to generate pseudo random numbers, in addition to VARIANCE reduction.

Question 20

Sensitivity Analyses quantify the _____ of input towards uncertainty

Answer 20

Sensitivity Analyses quantify the CONTRIBUTION of input towards uncertainty