@book{winn_model-based_2019,
title = {Model-{Based} {Machine} {Learning}},
isbn = {978-1-4987-5681-5},
abstract = {This book is unusual for a machine learning text book in that the authors do not review dozens of different algorithms. Instead they introduce all of the key ideas through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter therefore introduces one case study which is drawn from a real-world application that has been solved using a model-based approach.},
language = {en},
publisher = {Taylor \& Francis Incorporated},
author = {Winn, John Michael},
month = jun,
year = {2019},
note = {ZSCC: NoCitationData[s1]
Google-Books-ID: 84KRtgEACAAJ},
keywords = {Bayesian inference, Classical ML, Implementation}
}
@article{ghahramani_probabilistic_2015,
title = {Probabilistic machine learning and artificial intelligence},
volume = {521},
issn = {0028-0836, 1476-4687},
url = {http://www.nature.com/articles/nature14541},
doi = {10/gdxwhq},
language = {en},
number = {7553},
urldate = {2019-11-28},
journal = {Nature},
author = {Ghahramani, Zoubin},
month = may,
year = {2015},
note = {ZSCC: 0000611},
keywords = {Bayesian inference, Classical ML, Machine learning, Probabilistic programming},
pages = {452--459}
}
@incollection{wermuth_graphical_2001,
address = {Oxford},
title = {Graphical {Models}: {Overview}},
isbn = {978-0-08-043076-8},
shorttitle = {Graphical {Models}},
url = {http://www.sciencedirect.com/science/article/pii/B008043076700440X},
abstract = {Graphical Markov models provide a method of representing possibly complicated multivariate dependencies in such a way that the general qualitative features can be understood, that statistical independencies are highlighted, and that some properties can be derived directly. Variables are represented by the nodes of a graph. Pairs of nodes may be joined by an edge. Edges are directed if one variable is a response to the other variable considered as explanatory, but are undirected if the variables are on an equal footing. Absence of an edge typically implies statistical independence, conditional, or marginal depending on the kind of graph. The need for a number of types of graph arises because it is helpful to represent a number of different kinds of dependence structures. Of special importance are chain graphs in which variables are arranged in a sequence or chain of blocks, the variables in any one block being on an equal footing, some being possibly joint responses to variables in the past and some being jointly explanatory to variables in the future of the block considered. Some main properties of such systems are outlined, and recent research results are sketched. Suggestions for further reading are given. As an illustrative example, some analysis of data on the treatment of chronic pain is presented.},
language = {en},
urldate = {2019-11-22},
booktitle = {International {Encyclopedia} of the {Social} \& {Behavioral} {Sciences}},
publisher = {Pergamon},
author = {Wermuth, N. and Cox, D. R.},
editor = {Smelser, Neil J. and Baltes, Paul B.},
month = jan,
year = {2001},
doi = {10.1016/B0-08-043076-7/00440-X},
note = {ZSCC: NoCitationData[s0] },
keywords = {Bayesianism, Classical ML, Machine learning},
pages = {6379--6386}
}
@article{heckerman_tutorial_1995,
title = {A {Tutorial} on {Learning} {With} {Bayesian} {Networks}},
url = {https://www.microsoft.com/en-us/research/publication/a-tutorial-on-learning-with-bayesian-networks/},
abstract = {A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data analysis. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can …},
language = {en-US},
urldate = {2019-11-22},
author = {Heckerman, David},
month = mar,
year = {1995},
note = {ZSCC: 0000058},
keywords = {Bayesianism, Classical ML, Machine learning}
}