首页|Research Conducted at Carnegie Mellon University Has Updated Our Knowledge about Machine Learning (Likelihood-free Frequentist Inference: Bridging Classical Sta tistics and Machine Learning for Reliable Simulator-based Inference)
Research Conducted at Carnegie Mellon University Has Updated Our Knowledge about Machine Learning (Likelihood-free Frequentist Inference: Bridging Classical Sta tistics and Machine Learning for Reliable Simulator-based Inference)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news originatingfrom Pittsburgh, Pennsylvania, b y NewsRx correspondents, research stated, “Many areas of science rely onsimulat ors that implicitly encode intractable likelihood functions of complex systems. Classical statisticalmethods are poorly suited for these so-called likelihood-f ree inference (LFI) settings, especially outsideasymptotic and low-dimensional regimes.”
PittsburghPennsylvaniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningCa rnegie Mellon University
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