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tokens: observing epochs. Figure 5 shows that the parameter constraints are much tighter when we average over more observing epochs, emphasizing the importance of sampling the intrinsic source variability when attempting to constrain the model parameters with EHT observations. Figure 5 (a) has the poorest ( u , v ) coverage by far, but achieves the tightest constraint, while fixing both ( u , v ) coverage and sensitivity in Figures 5 (b) and (c) again show that simply sampling more epochs will tighten the parameter constraints significantly. Figure 5. Parameter estimation test with the same underlying parameters as Figure 4 . The three panels show the averaged likelihoods for 20 realizations each in three separate setups. The number of observations in each setup is chosen such that the raw overall sensitivity is held fixed across the three setups. The three panels correspond to (a) 920 epochs on the SMA–CARMA–SMT baseline, where previous EHT observations were done; (b) 61 epochs on baselines including SMA, CARMA, SMT, LMT, APEX, and SPT; (c) 4 epochs including the same baselines as panel (b) but with ALMA replacing APEX. These figures show that sampling the source variability is more important for parameter estimation than the raw sensitivity of the array or the ( u , v ) coverage. 3.4. Effect of Time Averaging In the parameter estimation test, we calculated likelihoods over parameter space with both time-dependent and averaged GRMHD images to compare the effect of time averaging. Figure 6 contrasts the likelihoods of time-resolved (black contours) and time-independent (red contours) inputs to our Bayesian method when the same number of mock observations are analyzed. Figures 6 (a) and (b) are the likelihoods assuming the early three-station baselines and the six-station EHT with ALMA, respectively. The figure shows that a much smaller region of parameter space is allowed when comparing the mock data to a time-averaged image. However, the best-fit parameters in the time-averaged case are offset from the true parameters, as is most clearly evident in Figure 6 (b). This demonstrates the false certainty implied by the small allowed parameter region. The time-resolved analysis, by contrast, delivers allowed parameter ranges that are consistent with the input simulation. Figure 6. Effect of time averaging in the parameter estimation test for (a) the early EHT baselines and (b) the full EHT baselines including ALMA. We use the identical parameters for the mock data as Figure 4 . The likelihoods of the averaged GRMHD image are overplotted in red contours. The likelihood using the averaged images underestimates the uncertainties of the inferred parameters. The best-fit parameters derived from the time-dependent and averaged images are offset from each other as well. 3.5. Model Selection We now use the Bayesian method to identify the relative posterior likelihood among several models, as discussed in Section 2.2 . For this test we generated mock visibilities