Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Applic
Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Applic
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混合专家和层次混合专家模型中的贝叶斯推理及其应用
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
Fengchun Peng
中科院分区：
文献类型：
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作者：
Fengchun Peng
摘要 由于上下文依赖性，将声学波形机器分类为语音事件通常很困难。这里通过使用一类模块化和分层系统（称为专家混合模型和分层专家混合模型）来研究多个说话人的元音识别任务。系统底层的统计模型是混合模型，其中混合系数和混合分量都是广义线性模型。完整的贝叶斯方法用作推理和预测的基础。使用马尔可夫链蒙特卡罗方法进行计算。这种方法的一个主要好处是能够从给定模型参数的任何函数的后验分布中获取样本。通过这种方式，可以获得比点估计所能提供的更多的信息。还避免了依赖后验的正常近似作为推理基础的需要。这在后面的情况下尤其重要......
Abstract Machine classification of acoustic waveforms as speech events is often difficult due to context dependencies. Here a vowel recognition task with multiple speakers is studied via the use of a class of modular and hierarchical systems referred to as mixtures-of-experts and hierarchical mixtures-of-experts models. The statistical model underlying the systems is a mixture model in which both the mixture coefficients and the mixture components are generalized linear models. A full Bayesian approach is used as a basis of inference and prediction. Computations are performed using Markov chain Monte Carlo methods. A key benefit of this approach is the ability to obtain a sample from the posterior distribution of any functional of the parameters of the given model. In this way, more information is obtained than can be provided by a point estimate. Also avoided is the need to rely on a normal approximation to the posterior as the basis of inference. This is particularly important in cases where the posteri...