28 papers · ranked by Valyu relevance
Camilo Fuentes-Beals, Alejandro Valdés-Jiménez, Gonzalo Riadi, Patricia M. Palagi
'Patricia M. Palagi'] Is it possible to learn and create a first Hidden Markov Model (HMM) without programming skills or understanding the algorithms in detail? In this concise tutorial, we present the HMM through the 2 general questions it was initially developed to answer and describe its elements. The HMM elements…
Jonathan H. Huggins, Frank Wood
This paper reviews recent advances in Bayesian nonparametric techniques for constructing and performing inference in infinite hidden Markov models. We focus on variants of Bayesian nonparametric hidden Markov models that enhance a posteriori state-persistence in particular. This paper also introduces a new Bayesian…
Andrew Miller, Fabio Di Troia, Mark Stamp
Momentum is a popular technique for improving convergence rates during gradient descent. In this research, we experiment with adding momentum to the Baum-Welch expectation-maximization algorithm for training Hidden Markov Models. We compare discrete Hidden Markov Models trained with and without momentum on English text…
Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza
In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process…
Khaled Safi, Wael Hosny Fouad Aly, Hassan Kanj, Tarek Khalifa + 2 more
Understanding the behavior of the human postural system has become a very attractive topic for many researchers. This system plays a crucial role in maintaining balance during both stationary and moving states. Parkinson’s disease (PD) is a prevalent degenerative movement disorder that significantly impacts human…
Yanxue Zhang, Dongmei Zhao, Jinxing Liu
The biggest difficulty of hidden Markov model applied to multistep attack is the determination of observations. Now the research of the determination of observations is still lacking, and it shows a certain degree of subjectivity. In this regard, we integrate the attack intentions and hidden Markov model (HMM) and…
Andreas Sand, Martin Kristiansen, Christian NS Pedersen, Thomas Mailund
'Thomas Mailund'] Background Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of…
Evan Sidrow, Nancy Heckman, Tess M. McRae, Beth L. Volpov + 4 more
'Andrew W. Trites' 'Sarah M.E. Fortune' 'Marie Auger-Méthé' 'Vitor Hugo Rodrigues Paiva'] Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal’s behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now…
Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga, Gustav Eje Henter
'Gustav Eje Henter'] Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidden Markov models can be used. However, these suffer from the computation of precision matrices and have a lot of…
Narges Manouchehri, Nizar Bouguila, Steve Ling
Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this…
Jennifer Pohle, Roland Langrock, Mihaela van der Schaar, Ruth King + 1 more
'Frants H. Jensen'] State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation…
Sofía Ruiz‐Suarez, Vianey Leos‐Barajas, Juan M. Morales
Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with temporal dependence as they take into account that observations close in time are likely generated from the same state (i.e. class). When information on the classes of the…
Claus Vogl, Mariia Karapetiants, Burçin Yıldırım, Hrönn Kjartansdóttir + 4 more
Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal…
Richard Fechner, Jens Dörpinghaus, Robert Rockenfeller, Jennifer Faber
'Jennifer Faber'] Background Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for description and modeling of disease progression. Deciphering potential underlying unknowns solely…
James D. Boyko, Jeremy M. Beaulieu
Hidden Markov models (HMM) have emerged as an important tool for understanding the evolution of characters that take on discrete states. Their flexibility and biological sensibility make them appealing for many phylogenetic comparative applications. Previously available packages placed unnecessary limits on the number…
Jiefu Li, Jung-Youn Lee, Li Liao
Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are…
Anjali R. Verma, Korak Kumar Ray, Maya Bodick, Colin D. Kinz-Thompson + 1 more
Time-dependent single-molecule experiments contain rich kinetic information about the functional dynamics of biomolecules. A key step in extracting this information is the application of kinetic models, such as hidden Markov models (HMMs), which characterize the molecular mechanism governing the experimental system.…
Luigi Catello, Ludovica Ruggiero, Lucia Schiavone, Mario Valentino
— The stock market presents a challenging environment for accurately predicting future stock prices due to its intricate and ever-changing nature. However, the utilization of advanced methodologies can significantly enhance the precision of stock price predictions. One such method is Hidden Markov Models (HMMs). HMMs…
Michalis K. Titsias, Christopher C. Holmes, Christopher Yau
Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable…
Tianshu Li, Giancarlo La Camera
The application of hidden Markov models (HMMs) to neural data has uncovered hidden states and signatures of neural dynamics that are relevant for sensory and cognitive processes. However, training an HMM on cortical data requires a careful handling of model selection, since models with more numerous hidden states…
Zeliha Kilic, Ioannis Sgouralis, Steve Pressé
The hidden Markov model (HMM) is a framework for time series analysis widely applied to single molecule experiments. It has traditionally been used to interpret signals generated by systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition…
Sergei Tarasov
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key…
Robert Arbon, Yanchen Zhu, Antonia S. J. S. Mey
Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data. These choices, or…
Anupam Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie Amaro
Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this…
Anupam Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie Amaro
Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this…
Sanjar Adilov
Generative neural networks have shown promising results in de novo drug design. Recent studies suggest that one of the efficient ways to produce novel molecules matching target properties is to model SMILES sequences using deep learning in a way similar to language modeling in natural language processing. In this…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
Chenxi Sui, Ziyang Jiang, Genesis Higueros, David Carlson + 1 more
High-performance batteries are poised for electrification of vehicles and therefore mitigate greenhouse gas emissions, which, in turn, promote a sustainable future. However, the design of optimized batteries is challenging due to the nonlinear governing physics and electrochemistry. Recent advancements have…