binary random variablesdiscrete datafirst‐order antedependencefirst‐order Markovgeneralized estimating equationsWe consider longitudinal discrete data that may be unequally spaced in time and may exhibit overdispersion, so that the variance of the outcome variable is inflated relative to its assumed ...
The notation X ∼ Bern(p) means that the binary random variable X follows a Bernoulli distribution with probability p. We use the operators |S| and ‖S‖ to denote the number of elements and the sum of the absolute value of elements in S, respectively. Section snippets System model...
Atrium follows Semantic Versioning and tries to be binary backward compatible within a major version (since 0.6.0). Until 2.0.0 this is only true for the API level, we reserve the right to break things on the logic and core level until then. Moreover, we follow the principle that a ...
Studies probing the effect of expectation on the neural and behavioural markers of perception often manipulate expectations in an artificial manner, an approach that cannot capture the impact of real-life experience on forming probabilistic priors. Exceptions to this have either (1) explored violations ...
Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precisi
We also reformulate the Tree-EP algorithm for the binary erasure channel (BEC) as a peeling-type algorithm (TEP) and we show that the algorithm has the same computational complexity as BP and it decodes a higher fraction of errors. We describe the TEP decoding process by a set of ...
We focus on binary case here that output of a network model is single channel and normalised by Sigmoid function. We first see pseudo labels as latent variables of a graphical model. The original pseudo labelling is an empirical estimation of E-step for estimating the latent variables, updating...
Findings suggest that the FN reflected a binary reward-related signal, with little relationship to reward expectation found in previous studies, in the absence of positive affective responses. Similar content being viewed by others The Brain’s Reward Response Occurs Even Without Actual Reward!
The\nmax-min expectation problem is polynomially solvable for constant d; we leave\nopen its complexity for variable d. We also show similar results for binary\nselection problems in which we must choose one distribution from each of n\npairs of distributions.David Eppstein...
To compare the performance of the proposed procedures a simulation study has been done based on some generated data sets. The data are generated from various distributions with various parameters to represent different cases of classification, including binary and multi-class classification. Further, ...