A Sequential Monte Carlo Method for Motif Discovery
نویسندگان
چکیده
منابع مشابه
A profile-based deterministic sequential Monte Carlo algorithm for motif discovery
MOTIVATION Conserved motifs often represent biological significance, providing insight on biological aspects such as gene transcription regulation, biomolecular secondary structure, presence of non-coding RNAs and evolution history. With the increasing number of sequenced genomic data, faster and more accurate tools are needed to automate the process of motif discovery. RESULTS We propose a d...
متن کاملSequential Noise Compensation by Sequential Monte Carlo Method
We present a sequential Monte Carlo method applied to additive noise compensation for robust speech recognition in time-varying noise. The method generates a set of samples according to the prior distribution given by clean speech models and noise prior evolved from previous estimation. An explicit model representing noise effects on speech features is used, so that an extended Kalman filter is...
متن کاملAn adaptive sequential Monte Carlo method for approximate Bayesian computation
Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Effective SMC algorithms that are currently available for ABC have a computational c...
متن کاملBayesian Phylogenetic Inference using a Combinatorial Sequential Monte Carlo Method
The application of Bayesian methods to large scale phylogenetics problems is increasingly limited by computational issues, motivating the development of methods that can complement existing Markov Chain Monte Carlo (MCMC) schemes. Sequential Monte Carlo (SMC) methods are approximate inference algorithms that have become very popular for time series models. Such methods have been recently develo...
متن کاملSequential Monte Carlo Samplers
In this paper, we propose a methodology to sample sequentially from a sequence of probability distributions known up to a normalizing constant and defined on a common space. These probability distributions are approximated by a cloud of weighted random samples which are propagated over time using Sequential Monte Carlo methods. This methodology allows us to derive simple algorithms to make para...
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: IEEE Transactions on Signal Processing
سال: 2008
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2008.926194