Resolution:
## Figure 3.
K. We simulated and analyzed data with varying levels of measurement noise and numbers
of measurements/cell/channel. The locations visited by MCMC walks (dots) for the three
noise levels (A) and the three numbers of measurements/cell/channel (B) show that
the highly probable region grows as measurement noise increases and as the number
of measurements decreases. The 'True Value' (black and white spot) indicates values
used to generate the data. Histograms of the locations visited by the walks (insets,
histograms smoothed for clarity) approximate the corresponding posterior probability
distributions for _{d }estimates reflect data quality and quantityK, with true values indicated by black lines. The plots of error vs. measurement noise (C) and error vs. amount of data (D) illustrate that, in general,
accuracy decreases with increasing noise and decreasing number of measurements although
the mean of the error remains centered at zero. Even when the true value is unknown,
the relative uncertainty of the parameter estimate (coefficient of variation of locations
visited by a walk) is measurable; it also grows with increasing noise (E) and decreasing
number of measurements/cell/channel (F). In (C-F), error bars are mean ± SD. 50 data
sets were analyzed for each noise level or number of measurements, and each dataset
was analyzed once with a random walk running for 20,000 steps, starting once the walk
converged. Except when otherwise indicated, data had 10 measurements/cell/channel
and 5% added Gaussian noise. For other parameters, see Methods.
_{d}Lichten and Swain |