Statistics and Probability Theory : In Pursuit of Engineering Decision Support / by Faber, M. H. (Michael Havbro), author 656113

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Fast and accurate detection of Identical-By-Descent segment sharing in large genomic datasets by Saada, J

18.217 Graph Theory and Additive Combinatorics, Fall 2019 by Zhao, Yufei

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15.075 Applied Statistics, Spring 2003 by Newton, Elizabeth

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Combating China’s corruption through learning from Taobao’s business model. by Tan, Winifred Guan Xin., Soh, Rachel Mei Hui., Choo, Amelene Isabel.

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Inference for autoregressive and moving average models with extreme value distribution via simulation study by Samuel, Bako Sunday

“…To achieve our objectives, a stationary autoregressive and moving average models with Gumbel distributed innovation is proposed and we characterise the short-term dependence among maxima, arising from light-tailed Gumbel distribution over a range of sample sizes with varying degrees of dependence. …”
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Game theoretical, statistical and behavioural aspects of peer evaluation by Tan, Catherine Ser Ping

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Challenges and Solution of Online Learning and Assessment Amidst the COVID-19 Pandemic: A Case Study in Universiti Utara Malaysia (S/O 14793) by Mohd Nordin, Nur Rasyidah, Omar, Wafa, Mohd Ridzuan, Iliya Nurul Iman

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Multiscale approach to nematic liquid crystals via statistical field theory by Lu, Bing-Sui

“…We propose an approach to a multiscale problem in the theory of thermotropic uniaxial nematics based on the method of statistical field theory. This approach enables us to relate the coefficients A, B, C, L1, and L2 of the Landau-de Gennes free energy for the isotropic-nematic phase transition to the parameters of a molecular model of uniaxial nematics, which we take to be a lattice gas model of nematogenic molecules interacting via a short-ranged potential. …”
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Uncovering latent features in massive open online courses by Gillani, N

Refining learning models in grammatical inference by Wang, Xiangrui

“…Grammatical inference is a branch of computational learning theory that attacks the problem of learning grammatical models from string samples. …”
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Questionnaire data analysis using information geometry by Har-Shemesh, Omri, Quax, Rick, Lansing, J. Stephen, Sloot, Peter M. A.

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Modeling and forecasting customer demands by See, Tian Pau.

“…In this paper, I have made an attempt to seek what statistical models and forecasting techniques which are appropriate to support decision making in the operational level of supply chain while dealing with customer demands on short life cycle products to avoid undesired production condition. …”
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Uncertainty quantification framework for combined statistical spatial downscaling and temporal disaggregation for climate change impact studies on hydrology by Rajendran Queen Suraajini

“…The Statistical Downscaling Model (SDM) is the bridging model which is used to downscale the output from the General Circulation Model (GCM) for increasing the spatial resolution of future climate scenarios. …”
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MAS.622 / 1.126J Pattern Recognition & Analysis, Fall 2000 by Massachusetts Institute of Technology. Media Laboratory.

Subjects: “…machine and human learning…”
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A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics by Barthel Sorensen, B., Charalampopoulos, A., Zhang, S., Harrop, B. E., Leung, L. R., Sapsis, T. P.

“…Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. …”
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Topics in Bayesian machine learning for finance by Spears, T

“…We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory. …”

Hermes: Boosting the Performance of Machine-Learning-Based Intrusion Detection System through Geometric Feature Learning by Zhang, Chaoyu, Shi, Shanghao, Wang, Ning, Xu, Xiangxiang, Li, Shaoyu, Zheng, Lizhong, Marchany, Randy, Gardner, Mark, Hou, Y. Thomas, Lou, Wenjing

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Bayesian neural network language modeling for speech recognition by Xue, Boyang, Hu, Shoukang, Xu, Junhao, Geng, Mengzhe, Liu, Xunying, Meng, Helen

“…State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. …”
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Topological data analysis for fake news detection by Deng, Ran

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