Expands Hidden Markov Model to include Markov chain observations.
problem Handling Markov chain observations in Hidden Markov Models.
method Developed Expectation-Maximization algorithm and Viterbi algorithm analogs.
result Estimates transition probabilities for hidden states and observations.
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 non…
Hidden Markov Neural Networks balance adaptation and forgetting in time-series data.
problem Balancing adaptation to new data and forgetting outdated information in time-series forecasting.
method Modeling weights as hidden states of a Hidden Markov model, using a filtering algorithm for learning a variational approximation of the posterior distribution over weights, and employing sequential Bayes by Backprop with variational DropConnect for regularization.
result Achieves strong predictive performance and effective uncertainty quantification on various tasks.
The paper estimates key metrics for linear models with Markov or hidden Markov sources.
problem Estimating free energy, mutual information, and MMSE for linear models with specific signal priors.
method Replica analysis in statistical physics, focusing on Markov and hidden Markov sources.
result The linear model with Markov or hidden Markov sources can be simplified into decoupled AWGN channels.
This work speeds up fHMM analysis by tensor algebra.
problem Scalability issues in analyzing factorial hidden Markov models.
method Tensorized algorithms and scalable filtering methods.
result Significant improvement in computational performance.
This paper compares HMM and LSTM for time series forecasting.
problem Comparing HMM and LSTM for time series forecasting.
method Discretization techniques and various training methods.
result HMM can outperform LSTM with limited labeled data.
Investor selects portfolios based on news attention in a hidden Markov model.
problem Mean-variance portfolio selection in a dynamic attention context.
method Closed-loop equilibrium strategies via extended HJB equation and Markov chain approximation.
result Equilibrium strategies found through iterative algorithm and numerical examples.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
problem Computing distributions and decoding hidden state sequences in HMMs.
method Finite Markov chain imbedding (FMCI) and hybrid decoding.
result Hybrid decoding improves performance over traditional methods.
Novel non-parametric tree model learns tree distributions.
problem Learning distributions for tree-structured data.
method Bottom-up hidden tree Markov model with infinite states.
result Novel non-parametric generalization of hidden tree Markov model.
Modified asymmetric hidden Markov models for time series with autoregressive components.
problem Dynamic relationships between variables in time series data.
method Introducing an asymmetric autoregressive component to recent asymmetric hidden Markov models.
result The model can choose the optimal autoregressive order for better likelihood.
NoMoPy models noise as HMM/FHMM in Python.
problem Modeling noise in data.
method Approximate and exact EM algorithms, cross-validation, confidence region estimation.
result Validated on example problems.
Paper uses dynamic analysis to detect malware with PHMMs.
problem Malware detection using static and dynamic analysis techniques.
method Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs) trained on API call sequences.
result PHMMs outperform HMMs in malware detection.
Hidden Markov model predicts sepsis progression stages.
problem Sepsis diagnostic criteria fail to account for patient heterogeneity.
method Introduced a hidden Markov model to account for heterogeneity.
result Model uncovers patient's latent sepsis trajectory.
The paper solves option pricing and hedging for financial time series with hidden Markov models.
problem Option pricing and hedging for financial time series with hidden Markov models.
method Solves the discrete time mean-variance hedging problem for autoregressive hidden Markov models.
result The proposed model outperforms simpler models in out-of-sample hedging and option pricing.
Hierarchical hidden Markov models predict market trends in financial time series.
problem Misinterpretation of short-term price fluctuations as long-term trend changes.
method Hierarchical hidden Markov models to capture both short- and long-term trends.
result Hierarchical models provide a comprehensive picture of financial markets.
The report explores parameter estimation methods in HMRF and related models.
problem Estimating hyper-parameters in HMRF and related models.
method Metropolis-Hastings algorithm, MCMC, pseudo-likelihood approximation, MAP estimation, EM algorithm.
result Effective parameter estimation methods for HMRF and related models.
A new HMM model captures kernel dependencies using context-specific Bayesian networks.
problem Traditional HMMs struggle with non-Gaussian data and independence assumptions.
method Kernel density estimation with context-specific Bayesian networks.
result The proposed model outperforms related HMMs in likelihood and classification accuracy.
Hidden Markov Model predicts student performance in educational games.
problem Estimating student knowledge progression in educational games.
method Time series analysis using hidden Markov model with game features.
result State trajectories accurately predict student performance.
New algorithm for collective Gaussian hidden Markov models inference.
problem Inference of collective Gaussian hidden Markov models from aggregate data.
method Collective Gaussian forward-backward algorithm, extending Sinkhorn belief propagation.
result Convergence guarantee and applicability to single individual Kalman filter.
Study evaluates initialization strategies for infinite hidden Markov models.
problem Limited attention to initialization in infinite hidden Markov models.
method Systematically evaluated distance-based clustering, model-based, and uniform initializations.
result Distance-based clustering initializations consistently outperform other methods.
Researchers calculate Shannon entropy rates of hidden Markov processes efficiently.
problem No finite expression exists for Shannon entropy rates of hidden Markov processes.
method Developed an efficient method to calculate entropy rates and identify minimal predictive features.
result Entropy rates can be accurately calculated for hidden Markov processes.
Detects anomalies in multiple processes using hidden Markov models.
problem Detecting an anomalous process among many with hidden states.
method Sequential search strategy using ADHM algorithm.
result ADHM algorithm effectively leverages temporal correlations.
Kernel density estimators enhance Markov models with hidden states for complex data.
problem Modeling complex, non-Markovian processes with short-term dependencies.
method Kernel Density Estimation (KDE) for conditional distributions, hidden states for long-term dependencies.
result KDE-HMMs outperform traditional models on held-out data.
The study uses Bayesian Hidden Markov Models to predict cryptocurrency returns.
problem Predicting the volatility and trends of cryptocurrencies.
method Bayesian Hidden Markov Models with four states to capture different return characteristics.
result The NHHM model with four states outperforms other models in predicting cryptocurrency returns.
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent activity levels that drive transitions and emissions, and show how to estimate its parameters. Our construction is motivated by the problem of inferring human mobility on sub-daily time scales from, for example, mobile phone …
Optimized HMM using PSO overcomes constraints for better solutions.
problem Finding global optimal solutions for HMM parameters.
method Constrained Particle Swarm Optimization (PSO) to solve HMM parameters, re-normalization and re-mapping to enforce constraints.
result PSOHMM finds better solutions and converges faster than BWHMM.
Enhances sampling for complex hidden Markov models using ensemble MCMC.
problem Challenges in Bayesian inference for factorial hidden Markov models due to large latent variable space.
method Introduces ensemble MCMC with parallel tempering and genetic algorithm for efficient exploration.
result Improves sampling efficiency and mixing of existing samplers in various applications.
Proposes a new model for flood extent mapping.
problem Noise, obstacles, heterogeneity, and spatial dependency issues in traditional classification methods.
method Geographical hidden Markov tree, incorporating anisotropic spatial dependency.
result Outperforms multiple baselines in flood mapping.
Quantum models learn sequential data more efficiently.
problem Learning hidden quantum Markov models efficiently.
method Relaxing constraints for quantum circuit simulation, reformulating HQMMs, and developing a learning algorithm.
result Quantum models can learn sequential data with fewer states and higher accuracy.
The Viterbi process can be extended indefinitely in a pairwise Markov model.
problem Estimating hidden chains in pairwise Markov models.
method Construction of barriers to ensure Viterbi path goes through states.
result The Viterbi process is regenerative in the PMM.
New algorithm processes Riemannian data more efficiently.
problem High memory usage and slow speed in previous Riemannian HMMs.
method Online algorithm based on Baum-Welch adapted for Riemannian manifolds.
result Significant improvements in speed and efficiency.
The generic identification problem is to decide whether a stochastic process (Xt) is a hidden Markov process and if yes to infer its parameters for all but a subset of parametrizations that form a lower-dimensional subvariety in parameter space. Partial answers so far available depend on extra assumptions on the pro…
Stochastic variational inference for collapsed models has recently been successfully applied to large scale topic modelling. In this paper, we propose a stochastic collapsed variational inference algorithm for hidden Markov models, in a sequential data setting. Given a collapsed hidden Markov Model, we break its long M…
Enhanced HMM for keystroke dynamics improves biometric accuracy.
problem Improving biometric authentication through keystroke dynamics.
method Partially observable hidden Markov model with user state assumption.
result The model outperforms standard HMM and other anomaly detectors.
Hybrid model improves traffic flow prediction accuracy.
problem Predicting traffic flow with high accuracy in short-term future.
method A hybrid model combining hidden Markov model and LSTM.
result Significant performance gains over conventional methods.
Study investigates estimation error in EMHMM simulations.
problem Estimation error in Hidden Markov Models (HMMs) with EMHMM.
method Simulation study using variational Bayesian inference.
result KL divergence and L1-norm relate to estimation error and ground-truth HMM parameters.
New algorithm optimizes complex model selection for non-homogeneous hidden Markov models.
problem Complex combinatorial optimization problem for model selection in NHHMMs.
method Adaptive simulated annealing EM algorithm (ASA-EM).
result Joint optimization of models and parameters for a criterion of interest.
A simple linear algebraic explanation of the algorithm in "A Spectral Algorithm for Learning Hidden Markov Models" (COLT 2009). Most of the content is in Figure 2; the text just makes everything precise in four nearly-trivial claims.
New method infers hidden states in continuous-time phenomena better than traditional models.
problem Traditional HSMM's are limited to discrete time grids and cannot handle irregularly spaced data.
method Formulated integro-differential forward and backward equations for CTSMC's, introduced scalable Viterbi-type algorithm.
result Efficiently solved equations for posterior marginals and path estimates.
Modeling disease progression in irregularly observed patients.
problem Irregular patient observation in healthcare databases.
method Continuous-time hidden Markov model with generalized linear model.
result Interpretable model of healthcare utilization events.
Improved MCMC for rare events in hidden Markov models.
problem Slow inference and prediction for rare latent states in hidden Markov models.
method Targeted sub-sampling (TASS) over-samples rare latent states, reducing variance in gradient estimation.
result Substantial gains in predictive and inferential accuracy on real and synthetic examples.
Generalizes bits back coding for time-series models with latent Markov structures.
problem Efficiently compressing time-series data with latent Markov structures.
method Extends bits back coding to time-series models with latent Markov structures, including HMMs and LGSSMs.
result Effective for small scale models, promising for larger scale settings like video compression.
This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…
Framework models graph-connected entities with sparse shared HMMs.
problem Model sequential data from graph-connected entities.
method Sparse Mixture of Hidden Markov Models (HMMs) trained jointly with graph topology.
result Effectiveness and versatility demonstrated in experiments.
Infinite Hidden Markov Models (iHMM's) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system. However, due to the infinite-dimensional nature of transition dynamics performing inference in the iHMM is difficult. In th…
Continuous Hidden Markov Models for Equity Returns
problem Generating synthetic equity returns that match real return characteristics
method Continuous Hidden Markov Models
result Recovered volatility clustering and narrowed kurtosis gap
The paper uses HMM and LSTM for stock market trend analysis.
problem Predicting stock market trends using machine learning.
method Apply Hidden Markov Model and Long Short Term Memory to stock market data.
result The combination of GMM-HMM+LSTM and XGB-HMM+LSTM outperformed other models.
A new model separates persistence and transition priors in HDP-HMM.
problem Limitation of sticky HDP-HMM in expressing different persistence strengths.
method Developed a disentangled sticky HDP-HMM (DS-HDP-HMM) with novel Gibbs sampling algorithms.
result DS-HDP-HMM outperforms sticky HDP-HMM and HDP-HMM on synthetic and real data.