Abstract or Introduction
Mixtures of experts models consist of a set of experts, which model conditional probabilistic processes, and a gate which combines the probabilities of the experts. The probabilistic basis for the mixture of experts is that of a mixture model in which the experts form the input conditional mixture components while the gate outputs form the input conditional mixture weights. A straightforward generalisation of ME models is the hierarchical mixtures of experts (HME) class of models, in which each expert is made up of a mixture of experts in a recursive fashion.
This principle states that complex problems can be better solved by decomposing them into smaller tasks. In mixtures of experts the assumption is that there are separate processes in the underlying process of generating the data. Modelling of these processes is performed by the experts while the decision of which process to use is modelled by the gate.
Mixtures of experts have many connections with other algorithms such as tree-based methods, mixture models and switching regression. In this, I review the paper by Rasmussen and Ghahramani to see how closely the mixtures of experts model resembles these other algorithms, and what is novel about it. The aim of this review is to adopt the method used in the current article to local precipitation data.
- Quote paper
- Jula Kabeto Bunkure (Author), 2020, Mixture of expert models. Statistical analysis method, Munich, GRIN Verlag, https://www.grin.com/document/595710
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We propose a dynamic model to analyze polychotomous data subject to temporal variation. In particular, we propose to model categorized levels of Temperature,C02, Methane,rainfalland others across time. Our model assumes that the observed category is related to an underlying latent continuous variable, which is modelled according to a power transformation of a Gaussian latent process, centered on a predictor that assigns dynamic effects to observable covariates. The inference procedure is based on the Bayesian paradigm and makes use of Markov chain Monte Carlo methods. We analyze artificial sets of data and daily measurements of rainfall in Rio de Janeiro, Brazil. When compared to the fitting of the actual observed volume of rainfall, our categorized model seems to recover well the structure of the data.
This method is motivated by a concept widespread in the Computer Science field (Korf, 1987), that is, if a problem may be separated into smaller subproblems, it might be easier to solve the subproblems. Moreover, the prediction accuracy is supposed to be improved through the combination of multiple individual estimates (Waterhouse, 1997). Thus, the general MEM framework specifies that a prediction is made up of a series of predictions from separate models, or experts, each of them weighted by a quantity determined by a so called gating function.
However, when building a MEM, many important decisions ought to be made.