| CSU | Hayward | |
|---|---|---|
| Statistics | Department |
Symbols Used in "Elementary Uses of the Gibbs Sampler: Applications to Medical Screening Tests"
STATS #27, Winter 2000, by Suess, Fraser, and Trumbo
| Character | Name | Definition or Usage |
| A | Observed count, #(T=1) | |
| a, b | alpha, beta | Parameters of beta prior distribution. Transition probabilities of a 2-state Markov Chain |
| B | Observed count, #(T=0) | |
| D | Random variable: 1 if virus present, 0 otherwise | |
| d | delta | Predictive value of negative test: P(D=0|T=0); d* = 1 – d |
| h | eta | Sensitivity: P(T=1|D=1); h* = 1 – h |
| g | gamma | Predictive value of positive test: P(D=1|T=1): g* = 1 – g |
| m | Simulation step number | |
| M1 | Number of simulation steps until burn in | |
| M2 | Total number of simulation steps | |
| N | Sample size | |
| p | Estimate of p | |
| p | pi | Prevalence: P(D=1); p* = 1 – p |
| P | Pi | Random variable for prevalence in Gibbs Sampler |
| t | Estimate of t | |
| T | Random variable: 1 if positive test, 0 otherwise | |
| t | tau | Probability of positive test: P(T=1) |
| q | theta | Specificity: P(T=0|D=0); q* = 1 – q |
| X | Observed or simulated count, #(T=1, D=1) | |
| Y | Observed or simulated count, #(T=0, D=1) |