Results 41 to 50 of about 7,474,817 (334)
Unsupervised Neural Hidden Markov Models [PDF]
SPNLP@EMNLP, 2016 In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag in- duction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a ...
Ke M. Tran +4 more
semanticscholar +1 more source
Perfect posterior simulation for mixture and hidden Markov models [PDF]
, 2010 In this paper we present an application of the read-once coupling from the past algorithm to problems in Bayesian inference for latent statistical models.
Berthelsen, Kasper Klitgaard +6 more
core +1 more source
Scoring hidden Markov models [PDF]
Bioinformatics, 1997 Statistical sequence comparison techniques, such as hidden Markov models and generalized profiles, calculate the probability that a sequence was generated by a given model. Log-odds scoring is a means of evaluating this probability by comparing it to a null hypothesis, usually a simpler statistical model intended to represent the universe of sequences ...
Christian Barrett +2 more
openaire +3 more sources
PDA-BCJR algorithm for factorial hidden Markov models with application to MIMO equalisation [PDF]
, 2006 In this paper we develop an efficient algorithm for inference in Factorial Hidden Markov Models (FHMM), which is particularly suitable for turbo equalisation in Multiple Input-Multiple Output (MIMO) systems.
Piechocki, Robert +7 more
core +1 more source
A coupled hidden Markov model for disease interactions [PDF]
, 2012 To investigate interactions between parasite species in a host, a population of field voles was studied longitudinally, with presence or absence of six different parasites measured repeatedly.
Telfer, S.E. +11 more
core +1 more source
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Advanced Engineering Materials, EarlyView.Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 more
wiley +1 more source
Hidden Markov models for stochastic thermodynamics
New Journal of Physics, 2015 The formalism of state estimation and hidden Markov models can simplify and clarify the discussion of stochastic thermodynamics in the presence of feedback and measurement errors.
John Bechhoefer
doaj +1 more source
Duration modeling with hidden Markov models [PDF]
The Journal of the Acoustical Society of America, 1993 In hidden Markov modeling (HMM) of speech signals, the statistics of speech characteristics are represented by HMM parameters after the HMM training. This procedure is purely statistical. This study concerns the incorporation of explicit knowledge into the HMM training. Therefore one specific parameter, i.e., segment duration, was selected. In order to
ten Bosch, L.F.M. +2 more
openaire +2 more sources
A statistical multiresolution approach for face recognition using structural hidden Markov models [PDF]
, 2007 This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM).
A. Amira +12 more
core +1 more source
Recent Advances of Slip Sensors for Smart Robotics
Advanced Materials Technologies, EarlyView.This review summarizes recent progress in robotic slip sensors across mechanical, electrical, thermal, optical, magnetic, and acoustic mechanisms, offering a comprehensive reference for the selection of slip sensors in robotic applications. In addition, current challenges and emerging trends are identified to advance the development of robust, adaptive,
Xingyu Zhang +8 more
wiley +1 more source