Diagnostic clinical labs are frequently requisitioned by healthcare providers to perform tests of medical risk factors of ...
Abstract: In this article, the stabilization problem is investigated for a class of networked control systems with random clock offsets and consecutive packet dropouts. Different from the existing ...
If a new pathogen causes a large epidemic, then it might “burn out” before causing a second epidemic. The burnout probability can be estimated from large numbers of computationally intensive ...
Probability theory is indispensable in computer science: It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory ...
Abstract: We study the following problem: two agents Alice and Bob are connected to each other by independent discrete memoryless channels. They wish to generate common randomness, i.e. agree on a ...
Predicting how complex stochastic systems respond to small external perturbations is central in physics, climate science, and engineering. We combine the generalized fluctuation–dissipation theorem ...
3.16 Exercises 4 Generating Random Variables 4.1 Inverse Transform Method 4.1.1 The Continuous Case 4.1.2 The Discrete Case 4.2 Accept/Reject Method 4.2.1 Discrete Case 4.2.2 Continuous Case 4.2.3 ...
Discrete stochastic processes (DSP) are instrumental for modeling the dynamics of probabilistic systems and have a wide spectrum of applications in science and engineering. DSPs are usually analyzed ...
Finally, the large sample sizes with LSAs provide a statistical power for analyses that allows detection on the individual level of even small effects, even if subsamples of the original population ...
Humans can meaningfully express their confidence about uncertain events. Normatively, these beliefs should correspond to Bayesian probabilities. However, it is unclear whether the normative theory ...
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