Bayesian Modelling in Machine Learning: A Tutorial Review
Related publications (33)
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Bayesian statistics is concerned with the integration of new information obtained through observations with prior knowledge, and accordingly, is often related to information theory (Jospin 2022). Recursive Bayesian estimation methods, such as Kalman Filter ...
Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predict ...
The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preve ...
In this thesis, we assess a new framework called UMIN on a data-driven optimization problem. Such a problem happens recurrently in real life and can quickly become dicult to model when the input has a high dimensionality as images for instance. From the ar ...
We study the computations that Bayesian agents undertake when exchanging opinions over a network. The agents act repeatedly on their private information and take myopic actions that maximize their expected utility according to a fully rational posterior be ...
The increasing integration of intermittent renewable generation, especially at the distribution level, necessitates advanced planning and optimisation methodologies contingent on the knowledge of the admittance matrix, capturing the topology and line param ...
Robotization and industrial automation are important fields of research and development in engineering. At micro scales, industrial automation development requires specific tools to handle components and perform precise assembly, etc. Therefore, the develo ...
The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed ...
Understanding the diffusion patterns of sequences of interdependent events is a central question for a variety of disciplines. Temporal point processes are a class of elegant and powerful models of such sequences; these processes have become popular across ...
Monitoring and interpreting structural response using structural-identification methodologies improves understanding of civil-infrastructure behavior. New sensing devices and inexpensive computation has made model-based data interpretation feasible in engi ...