A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Expands Hidden Markov Model to include Markov chain 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.
The paper estimates key metrics for linear models with Markov or hidden Markov sources.
This work speeds up fHMM analysis by tensor algebra.
This paper compares HMM and LSTM for time series forecasting.
Investor selects portfolios based on news attention in a hidden Markov model.
The paper introduces FMCI and hybrid decoding for hidden Markov models.
Modified asymmetric hidden Markov models for time series with autoregressive components.
NoMoPy models noise as HMM/FHMM in Python.
Hierarchical hidden Markov models predict market trends in financial time series.
A new HMM model captures kernel dependencies using context-specific Bayesian networks.
New algorithm for collective Gaussian hidden Markov models inference.
Study evaluates initialization strategies for infinite hidden Markov models.
Researchers calculate Shannon entropy rates of hidden Markov processes efficiently.
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…
Detects anomalies in multiple processes using hidden Markov models.
We define a Hidden Markov Model (HMM) in which each hidden state has time-dependent 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 …
The study uses Bayesian Hidden Markov Models to predict cryptocurrency returns.
The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a …
New algorithm processes Riemannian data more efficiently.
The Viterbi process can be extended indefinitely in a pairwise Markov model.
The generic identification problem is to decide whether a stochastic process 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…
Hybrid model improves traffic flow prediction accuracy.
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 algorithm optimizes complex model selection for non-homogeneous hidden Markov models.
New method infers hidden states in continuous-time phenomena better than traditional models.
Generalizes bits back coding for time-series models with latent Markov structures.
This is a technical report which explores the estimation methodologies on hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random Field. In first section, we briefly investigate a theoretical framework on Metropolis-Hastings algorithm. Next, by using MH algorithm, we simulate the data from Ising model…
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…
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…
As one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Opt…
Continuous Hidden Markov Models for Equity Returns
The paper uses HMM and LSTM for stock market trend analysis.
A new model separates persistence and transition priors in HDP-HMM.
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…
Hidden Markov Model helps track asymptomatic carriers in pandemic.
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particul…
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…
Hidden Quantum Markov Models (HQMMs) can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contributions: (1) we show how classical hidden Markov models (HMMs) can be simulated on a quantum circuit, (2) we reformulate HQMMs by relaxing th…
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 output units, given t…
This paper tackles hidden state inference for HMMs using particle filtering.
Study on the limits of learning HMM parameters under various conditions.
Estimates dependent parameters using Markovian dependence with shrinkage.
We explore a framework called boosted Markov networks to combine the learning capacity of boosting and the rich modeling semantics of Markov networks and applying the framework for video-based activity recognition. Importantly, we extend the framework to incorporate hidden variables. We show how the framework can be ap…
Develops a more flexible HDP-HMM for temporal data segmentation.
Hidden Markov Chains and Linear-chain CRFs are equivalent.