In ICU units with shorter underlying mean lengths of stay, the potential to increase the number of treated patients is even larger.
We estimated the probabilities in table 4 using the natural log of measurements of systolic blood pressure to normalise the data, assuming no change in the underlying mean blood pressure of the group and a true blood pressure of either 120 or 130 mm Hg at baseline.
Instead, we calculated the probability that this combination of spikes would occur for a range of underlying mean firing rates (e.g. 2, 2.25, 2.5, etc. spikes/presentation).
Likewise, we estimated the probabilities in table 5 assuming no change in the underlying mean diastolic blood pressure of the group and a true blood pressure of either 85 or 90 mm Hg at baseline.
Given a probability distribution of underlying mean firing rates for each probe response, we can use this to simulate many re-runs of collecting the same data, thus generating bootstrapped statistics.
This is not enough to generate any reliable estimates of response variability, or to assess the accuracy of our estimate of the underlying mean firing rate.
For example, medical diagnosis of hypertension has traditionally focused on achieving reliable estimates of the underlying mean blood pressure.
Next, for each measured response to the probe, for a given probe/conditioner combination, we used this spike count distribution to estimate the likelihood of different underlying mean firing rates.
We assumed that S k follows a lognormal distribution, suggesting that a marginal likelihood of X is where φ represents the standard normal density, and μ s and σ s are the underlying mean and standard deviation, respectively, of the log(S) distribution.
MIMLwel assumes that highly relevant labels usually share common instances, and the underlying class means of bags for each label are separated with a large margin [ 18].
