No, it does not. In sampling-based inference, the necessary computation time grows linearly with the demanded sample size, not exponentially. There may be diminishing returns to increasingly accurate probabilities, but that’s a fact about your utility function rather than an exponential increase in necessary computational power.
This precise switch, from an exponential computational cost growth-curve to a linear one, is why sampling-based inference has given us a renaissance in Bayesian statistics.
There may be diminishing returns to increasingly accurate probabilities, but that’s a fact about your utility function
This has nothing to do with utility functions.
Sample size is a linear function of the CPU time, but the accuracy of the estimates is NOT a linear function of sample size. In fact, there are huge diminishing returns to large sample sizes.
No, it does not. In sampling-based inference, the necessary computation time grows linearly with the demanded sample size, not exponentially. There may be diminishing returns to increasingly accurate probabilities, but that’s a fact about your utility function rather than an exponential increase in necessary computational power.
This precise switch, from an exponential computational cost growth-curve to a linear one, is why sampling-based inference has given us a renaissance in Bayesian statistics.
This has nothing to do with utility functions.
Sample size is a linear function of the CPU time, but the accuracy of the estimates is NOT a linear function of sample size. In fact, there are huge diminishing returns to large sample sizes.
Ah, ok, fair enough on that one.