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Bayesian Experiment Processing Capacity Estimator

The Bayesian Experiment Processing Capacity Estimator converts per-worker experiment throughput into an estimated total processing capacity over a chosen operating period. It is useful when Bayesian experimentation involves repeated simulations, model evaluations, posterior updates, batch analyses, or review tasks that consume a measurable number of processing units per worker-hour.

Enter the average units processed per worker-hour, the number of parallel workers, operating hours, and expected utilization. The calculator returns effective capacity after utilization, plus theoretical capacity at 100% utilization and idle-equivalent capacity. It is a planning model rather than a Bayesian statistical formula: the “Bayesian Experiment” context describes the workload, while the arithmetic focuses on operational throughput needed to run that workload reliably.

Experiment processing inputs

%
Result
—
Effective processing capacity
Theoretical capacity—
Utilization factor—
Idle-equivalent units—
Capacity per hour—

1. Measure unit throughput
Enter the average number of experiment-processing units one worker can complete in one productive hour.

2. Enter parallel capacity
Specify how many workers, processes, or equivalent compute slots can operate at the same time.

3. Set the operating window
Enter the total scheduled hours for the capacity period you are planning.

4. Apply utilization
Use utilization to account for setup time, queue gaps, maintenance, review, or other non-productive time.

5. Use effective capacity
Plan workload against the effective result rather than the 100% theoretical figure when utilization is below full capacity.

Theoretical capacity = units per worker-hour × workers × hours Effective capacity = theoretical capacity × utilization / 100 Idle-equivalent units = theoretical capacity − effective capacity Effective capacity per hour = units per worker-hour × workers × utilization / 100

All inputs are assumed to describe the same type of processing unit and the same operating period. The model assumes average throughput and utilization remain reasonably stable across the period.

What the result means

Effective processing capacity is the number of experiment-processing units expected to be completed during the entered operating window after utilization losses.

Real workloads can vary because of queueing, heterogeneous task sizes, compute throttling, or human review time. Treat this as a planning estimate.

Given: 120 units per worker-hour, 8 parallel workers, 10 operating hours, and 80% utilization.

Calculation: Theoretical capacity = 120 × 8 × 10 = 9,600 units. Effective capacity = 9,600 × 0.80 = 7,680 units. Idle-equivalent capacity = 9,600 − 7,680 = 1,920 units.

Result: Estimated effective processing capacity = 7,680 units, or 768 units per operating hour.

This gives a practical workload ceiling if the assumed average rate and utilization are representative.

What should count as one processing unit?

Use a unit that is consistent across the workload, such as one simulation, one model evaluation, one posterior update batch, or one reviewed result. Do not mix units with very different effort unless the rate has already been normalized.

Why include utilization instead of using the full theoretical capacity?

Scheduled capacity is rarely productive every minute. Utilization lets you account for expected downtime, setup, queue gaps, and other losses without changing the base throughput rate.

Can I use CPU cores or containers as workers?

Yes, if each worker unit has a comparable throughput rate and the workload can actually run in parallel. Shared bottlenecks can make real scaling less than linear.

What if throughput changes during the run?

Use a weighted average rate or calculate separate periods and add their capacities. A single average is less reliable when processing speed varies substantially over time.

Does this estimate statistical sample size?

No. It estimates operational throughput for processing experiment work. Statistical sample size calculators address how much data is needed for inference, which is a different question.