Our passion is modelling the world with Bayesian networks and causal models. We can help you define your problem, conceptualise, integrate expert knowledge, leverage big and small data, analyse risk and make models that provide key insights that support your most difficult and uncertain decisions. ...
Bayesian functional data analysisGARCH modelsSport analyticsLatent factor modellingThe use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in understanding and predicting an athlete's ...
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INTRODUCTION Random forests (Breiman, 2001; Breiman, 2001b) occupies a leading position amongst ensemble models and have shown to be very successful in data mining and analytics competitions such as KDD Cup (Lichman, 2013) and Kaggle (2016). One of the reasons for its success is that each ...
In the spectrum of Bayesian methods, there are two main flavours. Let’s call the firststatistical modellingand the secondprobabilistic machine learning. The latter contains the so-called nonparametric approaches. Modelling happens when data is scarce and precious and hard to obtain, for example in...
Fig. 2. Phases of ML modelling. In the training step, data is trained to incrementally improve the model’s ability for predicting the output. Once the training is complete, the built model is tested against data that has never been used for training and is evaluated to judge how the mode...
In clinical practice, a plethora of medical examinations are conducted to assess the state of a patient’s pathology producing a variety of clinical data. However, investigation of these data faces two major challenges. Firstly, we lack the knowledge of
His research focuses on applications of machine learning and statistics for data analytics, with a focus on deep neural networks and Bayesian inference. Stefan Feuerriegelis an assistant professor for management information systems at ETH Zurich. His research focuses on cognitive information systems and ...
Aguilera PA, Fernández A, Fernández R, Rumí R, Salmerón A (2011) Bayesian Networks in environmental modelling. Environ Model Softw 26(12):1376–1388 MATHGoogle Scholar Weber P, Medina-Oliva G, Simon C, Iung B (2012) Overview on Bayesian Networks applications for dependability, risk analysi...