![]() These include combinatorial optimization tasks such as maximum aposteriori (MAP) which finds the most likely configuration of the variables, or marginalization tasks that compute the normalization constants or marginal probabilities. Getting accurate inference from data typically involves challenging computational problems of optimization or estimation in highly dimensional spaces. Inference, such as making predictions or decisions, is perhaps the most fundamental issue for any graphical model. What technology problem will I help solve? Have contributors create new algorithms implementations or improve existing ones is paramount to achieving this goal. We expect this framework could be regarded as the gold standard in the artificial intelligence research community. The long-term objective of this project is to develop an open, easy-to-use, extensible framework to facilitate efficient exact and approximate probabilistic inference over graphical models. For MAP and Marginal MAP inference, Merlin employs advanced, search-based algorithms that exploit problem decomposition by traversing the AND/OR search space associated with graphical models. Merlin implements the classic Loopy Belief Propagation (LBP) algorithm as well as more advanced generalized belief propagation algorithms, such as Iterative Join-Graph Propagation (IJGP) and Weighted Mini-Bucket Elimination (WMB). It supports the most common inference tasks such as computing the partition function or probability of evidence (PR), posterior marginals (MAR), as well as finding MAP (maximum a posteriori or most probable explanation) and Marginal MAP configurations. Merlin is an extensible C++ library that implements state-of-the-art exact and approximate algorithms for inference over probabilistic graphical models. To tackle these problems in the future, we must accelerate the quest for more efficient, scalable inference algorithms capable of exploiting the underlying structure of the problem and of harnessing the computational power of the cloud. These models use graphs (directed or undirected) to capture the structure of extremely complex problems often involving hundreds or even many thousands of interacting variables.Ĭalculating the relevant probabilities (also known as inference) in a graphical model involves challenging computational problems of optimization and estimation in highly dimensional spaces and, therefore, becomes a practical issue in many situations. Probabilistic graphical models (or graphical models for short) allow systems and businesses to address these challenges in a unified framework. ![]() With a joint multiple longitudinal and competing risks survival model.Many real-world problems in artificial intelligence, computer vision, robotics, computer systems, computational biology, and natural language processing require systems to reason about highly uncertain, structured or unstructured data, and draw global insights from local observations. Theįlexibility of merlin is illustrated using an example in patients followed upĪfter heart valve replacement, beginning with a linear model, and finishing User-defined families, making merlin ideal for methodological research. To be made from even the most complex models. rlin function allows for individual and population level predictions The gradient and shared random effects, are available in order to link theĭifferent outcomes in a biologically plausible way. A wide variety of link functions, including the expected value, Which allows for the estimation of models with unlimited numbers of continuous,īinary, count and time-to-event outcomes, with unlimited levels of nested Information to be incorporated into one model. However, there is limited software that allows all of this Possibly censored time-to-event outcomes and baseline characteristics areĪvailable. Increasingly, multiple longitudinal biomarker measurements, Crowther Download PDF Abstract: The R package merlin performs flexible joint modelling of hierarchical Martin and Alessandro Gasparini and Michael J. ![]() Download a PDF of the paper titled merlin: An R package for Mixed Effects Regression for Linear, Nonlinear and User-defined models, by Emma C.
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