We based our decision criteria on a probability function to consider only correct category outputs during the maintenance period [see Figure S1 for an analysis of the degree to which a classifier could estimate (again by using a threshold classifier output of >0.95) that neural activity for a particular trial and time bin could be simultaneously classified into both categories].
But the superforecasters drill down on much more specific probabilities, taking care to consider whether something really has a 62 or 63 percent chance of occurring.
In our analysis, we chose probabilities above 50% to consider only the almost invariable anatomical core of each single tract and not its periphery (Thiebaut de Schotten, ffytche et al. 2011).
Since GLE events do originate from outside this longitude range (albeit with lower probability), we need to consider major eruptions from other longitudes as well.
In order to make a controllable cluster decision that follows a certain criterion such as the Neyman-Pearson criterion or minimum error probability criterion, one factor to consider is the probability density function of the cluster's selective sensing data which is utilized to make the cluster decision.
When one wants to compare the probability of different events, say of selecting a black ball and selecting a white ball, it may be more convenient to consider probabilities to be terms in their own right.
These concepts are useful in a variety of situations: to summarize distributions, to consider probabilities of individual scores, to consider confidence intervals of population parameters, to compute a t test when asked to assess the statistical significance of a mean difference.
Authors' response: We agree that mapping a gene tree onto the ribosomal scaffold is a complex, non-trivial process that needs to consider probabilities of gene duplications, gene loss, and gene transfer.
Similar comments may be made about (Maroney 2009), while (Turgut 2009) managed to go further and deduce a stronger constraint, which implies the usual version of Landauer's principle, but without even needing to consider probability distributions over the input logical states.
Since it is difficult to calculate the probability of an event directly, we can use Bayesian theory to consider conditional probability of the event.
