Method for identifying a subset of components of a system
First Claim
1. A method of identifying a subset of components of a system based on data obtained from the system using at least one training sample from the system, the method comprising the steps of:
- obtaining a linear combination of components of the system and weightings of the linear combination of components, the weightings having values based on data obtained from the at least one training sample, the at least one training sample having a known feature;
obtaining a model of a probability distribution of the known feature, wherein the model is conditional on the linear combination of components;
obtaining a prior distribution for the weighting of the linear combination of the components, the prior distribution comprising a hyperprior having a high probability density close to zero, the hyperprior being such that it is based on a combined Gaussian distribution and Gamma hyperprior;
combining the prior distribution and the model to generate a posterior distribution; and
identifying the subset of components based on a set of the weightings that maximise the posterior distribution.
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Abstract
A method of identifying a subset of components of a system based on data obtained from the system using at least one training sample from the system, the method comprising the steps of: obtaining a linear combination of components of the system and weightings of the linear combination of components, the weightings having values based on data obtained from the at least one training sample, the at least one training sample having a known feature; obtaining a model of a probability distribution of the known feature, wherein the model is conditional on the linear combination of components; obtaining a prior distribution for the weighting of the linear combination of the components, the prior distribution comprising a hyperprior having a high probability density close to zero, the hyperprior being such that it is not a Jeffreys hyperprior, combining the prior distribution and the model to generate a posterior distribution; and identifying the subset of components based on a set of the weightings that maximise the posterior distribution.
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Citations
26 Claims
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1. A method of identifying a subset of components of a system based on data obtained from the system using at least one training sample from the system, the method comprising the steps of:
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obtaining a linear combination of components of the system and weightings of the linear combination of components, the weightings having values based on data obtained from the at least one training sample, the at least one training sample having a known feature;
obtaining a model of a probability distribution of the known feature, wherein the model is conditional on the linear combination of components;
obtaining a prior distribution for the weighting of the linear combination of the components, the prior distribution comprising a hyperprior having a high probability density close to zero, the hyperprior being such that it is based on a combined Gaussian distribution and Gamma hyperprior;
combining the prior distribution and the model to generate a posterior distribution; and
identifying the subset of components based on a set of the weightings that maximise the posterior distribution. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 21, 22, 23, 24)
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18. An apparatus for identifying a subset of components of a system from data generated from the system from a plurality of samples from the system, the subset being capable of being used to predict a feature of a test sample, the apparatus comprising:
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a processing means operable to;
obtain a linear combination of components of the system and obtain weightings of the linear combination of components, each of the weightings having a value based on data obtained from at least one training sample, the at least one training sample having a known feature;
obtaining a model of a probability distribution of a second feature, wherein the model is conditional on the linear combination of components;
obtaining a prior distribution for the weightings of the linear combination of the components, the prior distribution comprising an adjustable hyperprior which allows the prior probability mass close to zero to be varied wherein the hyperprior is based on a combined Gaussian distribution and Gamma hyperprior;
combining the prior distribution and the model to generate a posterior distribution; and
identifying the subset of components having component weights that maximize the posterior distribution. - View Dependent Claims (19)
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25. A computer program which, when executed by on a computing device, allows the computing device to carry out a method of identifying components from a system that are capable of being used to predict a feature of a test sample from the system, and wherein a linear combination of components and component weights is generated from data generated from a plurality of training samples, each training sample having a known feature, and a posterior distribution is generated by combining a prior distribution for the component weights comprising an adjustable hyperprior which allows the probability mass close to zero to be varied wherein the hyperprior is based on a combined Gaussian distribution and Gamma hyperprior, and a model that is conditional on the linear combination, to estimate component weights which maximise the posterior distribution.
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26. A method of identifying a subset of components of a biological system, the subset being capable of predicting a feature of a test sample from the biological system, the method comprising the steps of:
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obtaining a linear combination of components of the system and weightings of the linear combination of components, each of the weightings having a value based on data obtained from at least one training sample, the at least one training sample having a known feature;
obtaining a model of a probability distribution of the known feature, wherein the model is conditional on the linear combination of components;
obtaining a prior distribution for the weightings of the linear combination of the components, the prior distribution comprising an adjustable hyperprior which allows the probability mass close to zero to be varied;
combining the prior distribution and the model to generate a posterior distribution; and
identifying the subset of components based on the weightings that maximize the posterior distribution.
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Specification