Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...
Abstract: This article addresses the adaptive radar target detection problem in the presence of Gaussian interference with unknown statistical properties. To this end, the problem is first formulated ...
Abstract: Reconstructing bandlimited graph signals from a subset of noisy measurements is a fundamental challenge within the realm of signal processing. Historically, this problem has been approached ...
Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
This digital series—featuring scholars from CSIS Futures Lab and AI evaluation experts from Scale AI—explores how large language models approach critical foreign policy decision-making scenarios. This ...
Create a hybrid pricing model using the ICE model and traditional valuation Conclusion: A 'hybrid pricing model' that integrates the ICE model (expected value/attractiveness) and traditional valuation ...
A faster variant running a single density greedy yields a $1/2$ approximation (Algorithm 7). For the non-monotone case, the algorithm adopts the "density greedy + random deletion with fixed ...
Expectation-Maximization (EM) algorithm is used to find the gene expression values that maximize the likelihood function. Recovering multi-gene reads via MLE-EM model was previously used to quantify ...
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