arXiv research

Paper proposes KSHMM for short-term wind-speed forecasting.

A new HMM model captures kernel dependencies using context-specific Bayesian networks.

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.

Hidden semi-Markov models (HSMMs) are latent variable models which allow latent state persistence and can be viewed as a generalization of the popular hidden Markov models (HMMs). In this paper, we introduce a novel spectral algorithm to perform inference in HSMMs. Unlike expectation maximization (EM), our approach cor…

DenseHMM improves HMMs by learning dense representations that enable gradient-based optimization.

Paper improves spectral learning of HMMs to avoid local optima and improve robustness.

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic repr…

We establish upper bounds for the minimal number of hidden units for which a binary stochastic feedforward network with sigmoid activation probabilities and a single hidden layer is a universal approximator of Markov kernels. We show that each possible probabilistic assignment of the states of $n$ output units, given t…

Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity in spectral features as well as implicit anisotropic spatial dependency across class labels. In thi…

Scalable hybrid HMM with Gaussian Process for time-series data clustering.

The study examines Fisher information matrices and neural tangent kernels for simple ReLU networks with random weights.

Spectral deconfounding improves machine learning models by reducing hidden confounding effects.

Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically with the dimensionality of state space. However, in practice, these large MDPs typically incorporate a latent or hidden low-dimensional structu…

We develop a latent variable model and an efficient spectral algorithm motivated by the recent emergence of very large data sets of chromatin marks from multiple human cell types. A natural model for chromatin data in one cell type is a Hidden Markov Model (HMM); we model the relationship between multiple cell types by…

Continuous Hidden Markov Models for Equity Returns

Study of multi-armed bandits with state-switching rewards using Markov models.

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging…

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging…

Hidden Markov Models (HMMs) can be accurately approximated using co-occurrence frequencies of pairs and triples of observations by using a fast spectral method in contrast to the usual slow methods like EM or Gibbs sampling. We provide a new spectral method which significantly reduces the number of model parameters tha…

Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods, such as maximum-likelihood estimation and especially expectation-maximization, a…

This work considers the problem of learning the structure of multivariate linear tree models, which include a variety of directed tree graphical models with continuous, discrete, and mixed latent variables such as linear-Gaussian models, hidden Markov models, Gaussian mixture models, and Markov evolutionary trees. The …

Method infers causal structure from system behaviors using RKHS and kernel $ε$-machines.

Model detects market anomalies using a Hawkes process with hidden Markov chain.

New method for fluid approximation of CTMCs without population structure.

Expands Hidden Markov Model to include Markov chain observations.

Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing. Being generative rather than discriminative models, however, their classification performance is a drawback. In this paper we apply ideas fr…

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…

The paper estimates key metrics for linear models with Markov or hidden Markov sources.

The past decade has seen substantial work on the use of non-negative matrix factorization and its probabilistic counterparts for audio source separation. Although able to capture audio spectral structure well, these models neglect the non-stationarity and temporal dynamics that are important properties of audio. The re…

Hidden Markov Neural Networks balance adaptation and forgetting in time-series data.

A scalable Bayesian additive model for stellar flare detection using Gaussian process inference and hidden Markov models.

This paper compares HMM and LSTM for time series forecasting.

We introduce two kernels that extend the mean map, which embeds probability measures in Hilbert spaces. The generative mean map kernel (GMMK) is a smooth similarity measure between probabilistic models. The latent mean map kernel (LMMK) generalizes the non-iid formulation of Hilbert space embeddings of empirical distri…

Modified asymmetric hidden Markov models for time series with autoregressive components.

NoMoPy models noise as HMM/FHMM in Python.

This work speeds up fHMM analysis by tensor algebra.

Investor selects portfolios based on news attention in a hidden Markov model.

Paper uses dynamic analysis to detect malware with PHMMs.

The paper introduces FMCI and hybrid decoding for hidden Markov models.

Hierarchical hidden Markov models predict market trends in financial time series.

Hidden Markov Model predicts student performance in educational games.

New algorithm for collective Gaussian hidden Markov models inference.

Study evaluates initialization strategies for infinite hidden Markov models.

Neural networks and linear systems linked, revealing training loss and kernel limitations.

New algorithm clusters trajectories from multiple Markov chains with near-optimal error.

Hidden tree Markov models allow learning distributions for tree structured data while being interpretable as nondeterministic automata. We provide a concise summary of the main approaches in literature, focusing in particular on the causality assumptions introduced by the choice of a specific tree visit direction. We w…

The study uses Bayesian Hidden Markov Models to predict cryptocurrency returns.

We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computationally expensive for sequences of the scale of the mammalian genome. Recently developed spectral algorithms can learn parameters of latent va…