Plan how many Bayesian experiment updates a processing team or system can complete from its observation throughput. Enter the processing rate, observations required per experiment, parallel capacity, productive hours, and utilization allowance. The result separates theoretical observation volume from a realistic experiment count, helping you identify whether sample demand or operational throughput is the binding constraint.
Usable observations/day = rate × workers × hours × utilization. Completed experiments/day = floor(usable observations ÷ observations per experiment). Hours per experiment on one worker = observations per experiment ÷ rate.
The primary result is conditional on the values and statistical assumptions entered. Read the secondary results to understand uncertainty, workload, or design sensitivity before making a decision.
This planning estimate is not a substitute for a study protocol, domain review, or a method chosen for the final data distribution.
At 500 observations per worker-hour, four workers, eight hours, and 80% utilization, usable capacity is 12,800 observations per day. At 10,000 observations per experiment, capacity is 1 completed experiment per day with 2,800 observations left.
The calculator uses a standard statistical approximation. Small samples, highly skewed outcomes, or model misspecification can make the estimate less accurate.
Yes. A 90% setting gives a narrower interval or lower sample requirement, while a 95% setting provides more conservative coverage.
Use zero only where the input allows it. A rate calculation still needs a positive total or a defined prior so that division by zero is avoided.
No. It summarizes uncertainty or planning assumptions; study design, randomization, measurement quality, and confounding still matter.
Keep rates and intervals at the shown precision, but round required counts upward because a fraction of a sample or processing unit is not usable.
| Item | How it is used |
|---|---|
| Observed or planned sample | Sets the available evidence or workload. |
| Uncertainty setting | Controls interval width or rejection threshold. |
| Variability or rate | Translates the sample into uncertainty, power, or capacity. |