Continuous hidden process model for time series expression experiments
نویسندگان
چکیده
منابع مشابه
Continuous hidden process model for time series expression experiments
MOTIVATION When analyzing expression experiments, researchers are often interested in identifying the set of biological processes that are up- or down-regulated under the experimental condition studied. Current approaches, including clustering expression profiles and averaging the expression profiles of genes known to participate in specific processes, fail to provide an accurate estimate of th...
متن کاملKeyframe compression and decompression for time series data based on the continuous hidden Markov model
Memory of motion patterns as data comparison of a new motion pattern with the data and playback of one from the data are inevitably involved in the informa tion processing of intelligent robot systems Such com putation forms the computational foundation of learn ing acquisition recognition and generation process of intelligent robotic systems In this paper we propose to apply the continuous hid...
متن کاملMCMC for hidden continuous - time
Hidden Markov models have proved to be a very exible class of models, with many and diverse applications. Recently Markov chain Monte Carlo (MCMC) techniques have provided powerful computational tools to make inferences about the parameters of hidden Markov models, and about the unobserved Markov chain, when the chain is deened in discrete time. We present a general algorithm, based on reversib...
متن کاملVolatility: a hidden Markov process in financial time series.
Volatility characterizes the amplitude of price return fluctuations. It is a central magnitude in finance closely related to the risk of holding a certain asset. Despite its popularity on trading floors, volatility is unobservable and only the price is known. Diffusion theory has many common points with the research on volatility, the key of the analogy being that volatility is a time-dependent...
متن کاملDiscrete or Continuous-Time Hidden Markov Models for Count Time Series
In Hidden Markov Models (HMM) the probability distribution of response Yt (∀t = 1, 2, . . . , T ) at each observation time is conditionally specified on the current hidden or latent state Xt. The sequence of hidden states follows a first order time-homogeneous Markov chain. Discrete time or continuous time HMM are respectively specified by T ⊆ N or T ⊆ R (from now on DHMM and CHMM). In this wor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2007
ISSN: 1460-2059,1367-4803
DOI: 10.1093/bioinformatics/btm218