Constructing a prior distribution

Koen Derks

last modified: 07-11-2020

Bayesian statistics

Bayesian statistics allows you to incorporate existing information into the statistical analysis and revise this information using the information from the sample, possibly increasing your efficiency in the sampling procedure. For example, when you have information that indicates a low-risk profile for your client, you may require less evidence from the audit sampling procedures. Be aware that all information that you incorporate into the statistical analysis should be justified.

Prior probability distributions

Bayesian statistics incorporates existing information into the sampling procedure using a prior probability distribution that reflects your current knowledge about the misstatement in the population. The prior distribution is created using existing information, and is therefore generally created before the planning stage in the procedure.

Prior construction methods

An important question is how to incorporate various kinds of existing information into the prior distribution. jfa offers six methods to create a prior distribution. These methods are explained below.

First, let’s set some default options for the confidence, performance materiality, the likelihood, and the expected errors.

confidence <- 0.95        # 95% confidence
likelihood <- "binomial"  # Binomial likelihood
materiality <- 0.05       # Performance materiality of 5%
expectedError <- 0        # Zero errors expected in sample

[Default] No explicit information (method = "none")

You can refrain from incorporating explicit information in the prior distribution, and still use Bayesian statistics, using method = none. As an example, the code below incorporates no explicit information into a prior distribution

prior1 <- auditPrior(confidence, materiality, expectedError, likelihood, method = "none")
prior1
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               None 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 1) 
## # Implicit sample size:    0 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.95 
## # Precision:               NaN 
## # Mode:                    NaN 
## # Mean:                    0.5 
## # Median:                  0.5 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior1)

Audit Risk Model (method = "arm")

You can incorporate the risk assessments from the Audit Risk Model (inherent risk and internal control risk) using method = "arm in combination with the ir and cr arguments. As an example, the code below incorporates the information that the inherent risk is equal to 90% and that the internal control risk is equal to 60% into a prior distribution.

prior2 <- auditPrior(confidence, materiality, expectedError, method = "arm", likelihood = likelihood, ir = 0.9, cr = 0.6)
prior2
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               Inherent risk = 0.9; Internal control risk = 0.6; Detection risk = 0.093 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 13) 
## # Implicit sample size:    12 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.206 
## # Precision:               0.206 
## # Mode:                    0 
## # Mean:                    0.071 
## # Median:                  0.052 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior2)

Equal prior probabilities (method = "median")

You can incorporate the assumption that tolerable misstatement is equally likely as intolerable misstatement using method = "median". As an example, the code below incorporates this assumption into a prior distribution.

Note: This method requires that you specify a value for the materiality.

prior3 <- auditPrior(confidence, materiality, expectedError, method = "median", likelihood = likelihood)
prior3
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               p(T < 0.05) = p(T > 0.05) = 0.5 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 13.513) 
## # Implicit sample size:    12.513 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.199 
## # Precision:               0.199 
## # Mode:                    0 
## # Mean:                    0.069 
## # Median:                  0.05 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior3)

Custom prior probabilities (method = "hypotheses")

You can assign custom probabilities to the hypothesis of tolerable misstatement (using pHmin) and/or the hypotheses of intolerable misstatement (using pHplus) in combination with method = "hypotheses. As an example, the code below incorporates the information that the hypothesis of tolerable misstatement has a probability of 60% into a prior distribution.

Note: This method requires that you specify a value for the materiality and that the expected errors are zero.

prior4 <- auditPrior(confidence, materiality, expectedError, method = "hypotheses", likelihood = likelihood, pHmin = 0.6)
prior4
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               p(T < 0.05) = 0.6; p(T > 0.05) = 0.4 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 17.864) 
## # Implicit sample size:    16.864 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.154 
## # Precision:               0.154 
## # Mode:                    0 
## # Mean:                    0.053 
## # Median:                  0.038 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior4)

Earlier sample (method = "sample")

You can incorporate information from an earlier sample into the prior distribution using method = "sample in combination with sampleN and sampleK. As an example, the code below incorporates the information from an earlier sample of 30 items in which 0 misstatements were found into a prior distribution.

prior5 <- auditPrior(confidence, materiality, expectedError, likelihood = likelihood, method = "sample", sampleN = 30, sampleK = 0)
prior5
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               Earlier sample of 30 items with 0 errors 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 31) 
## # Implicit sample size:    30 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.092 
## # Precision:               0.092 
## # Mode:                    0 
## # Mean:                    0.031 
## # Median:                  0.022 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior5)

Weighted earlier sample (method = "factor")

You can incorporate information from last years results, weighted by a factor, into the prior distribution using method = "factor in combination with sampleN and sampleK. As an example, the code below incorporates the information from a last years results (a sample of 58 items in which 0 misstatements were found), weighted by a factor 0.7, into a prior distribution.

prior6 <- auditPrior(confidence, materiality, expectedError, method = "factor", likelihood = likelihood, sampleN = 58, sampleK = 0, factor = 0.7)
prior6
## # ------------------------------------------------------------
## #         jfa Prior Distribution Summary (Bayesian)
## # ------------------------------------------------------------
## # Input:
## #
## # Confidence:              95%    
## # Expected sample errors:  0%       
## # Likelihood:              binomial 
## # Specifics:               Earlier sample of 58 items with 0 errors weighted by 0.7 
## # ------------------------------------------------------------
## # Output: 
## #
## # Prior distribution:      beta(a = 1, ß = 41.6) 
## # Implicit sample size:    40.6 
## # Implicit errors:         0 
## # ------------------------------------------------------------
## # Statistics: 
## #
## # Upper bound:             0.069 
## # Precision:               0.069 
## # Mode:                    0 
## # Mean:                    0.023 
## # Median:                  0.017 
## # ------------------------------------------------------------

You can visually inspect the prior distribution using the plot() function.

plot(prior6)

Comparing efficiency through required sample sizes

To illustrate how the prior distribution can facilitate a more efficient audit, the table below lists the required sample sizes for the created priors after calling planning(confidence = confidence, expectedError = expectedError, likelihood = likelihood, materiality = materiality, prior = X), where X is the object returned by the auditPrior() function.

none arm median hypotheses sample factor
Required sample size 58 46 45 41 28 17