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.
Develops CLTs for Markov chain transition probabilities and policies.
A multi-task GP model tracks time-varying transition probabilities between two states.
Novel unsupervised feature selection method using multi-step Markov transition probability.
New insights into Markov chain geometry via positive transition measures.
Develops a new framework for conditional independence.
This paper proposes a stochastic model using the concept of Markov chains for the inter-state transitions of the millisecond order quasi-stable phase synchronized patterns or synchrostates, found in multi-channel Electroencephalogram (EEG) signals. First and second order transition probability matrices are estimated fo…
Expands Hidden Markov Model to include Markov chain observations.
A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix , a prior probability vector , and the state dependent output symbol error probability matrix . Several processes are utilized for estimating , and from a given empirically obtained or sim…
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed to change with time. We introduce an algorithm whose regret with respect to any policy in a comparison class grows as t…
We describe a generalization of the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) which is able to encode prior information that state transitions are more likely between "nearby" states. This is accomplished by defining a similarity function on the state space and scaling transition probabilities by pai…
Paper tackles identifying an odd arm in a multi-armed bandit with restless Markov processes and trembling hand.
A Longitudinal Attribute-Conditioned Neural Network (LANTERN) framework for modeling health-state transition probabilities in irregular longitudinal data.
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1. We derive a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump process, and ar…
The paper extends MS models with TVTP to U.S. Treasury yields, finding reliable regime dynamics but challenging TVTP identification.
Study analyzes order transitions in high, medium, and low market cap stocks using Markov chains.
Method determines credit transition matrix from cumulative default probabilities.
We study discretizations of polynomial processes using finite state Markov processes satisfying suitable moment matching conditions. The states of these Markov processes together with their transition probabilities can be interpreted as Markov cubature rules. The polynomial property allows us to study such rules using …
This paper proposes a multi-scale Markov-Switching GARCH model for EUR/USD volatility.
Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent states and mean parameter of the probability distribution of the responses (for a given state) depend on the set of covariates. A priori w…
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, which cannot be achieved by conventional methods, such as the Metropolis-Hastings algorithm or the heat bath algorithm (Gib…
We consider reinforcement learning in parameterized Markov Decision Processes (MDPs), where the parameterization may induce correlation across transition probabilities or rewards. Consequently, observing a particular state transition might yield useful information about other, unobserved, parts of the MDP. We present a…
We present two methodologies on the estimation of rating transition probabilities within Markov and non-Markov frameworks. We first estimate a continuous-time Markov chain using discrete (missing) data and derive a simpler expression for the Fisher information matrix, reducing the computational time needed for the Wald…
Study nonparametric estimator for Markov chain transition matrices in offline setting.
A new model separates persistence and transition priors in HDP-HMM.
TMTF improves time series visualization by separating dynamic regimes.
A low-rank tensor model simplifies multi-dimensional Markov chains.
A new algorithm for deep Q-learning with robustness to state transition uncertainty.
Novel algorithm for Markov decision processes using rank-one approximation.
We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem setting, we propose an algorithm using a sliding window approach and provide performance guarantees for the regret evaluated against the op…
In this paper we continue the study of the simulated stock market framework defined by the driving sentiment processes. We focus on the market environment driven by the buy/sell trading sentiment process of the Markov chain type. We apply the methodology of the Hidden Markov Models and the Recurrent Neural Networks to …
The problem of estimating an unknown discrete distribution from its samples is a fundamental tenet of statistical learning. Over the past decade, it attracted significant research effort and has been solved for a variety of divergence measures. Surprisingly, an equally important problem, estimating an unknown Markov ch…
We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable fo…
Study best arm identification in restless bandits with unknown TPMs.
Develops new Markov processes with switching rates and past dependence.
We introduce imprecise Markov semigroups to handle uncertainty in Markov processes.
Neural Markov models improve time series analysis by balancing deep learning and classical models.
We consider the problem of constructing an appropriate multivariate model for the study of the counterparty credit risk in credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However the markovian assumption may be inappropriate for the study of the dyna…
Enhanced Markov chain sampler learns network statistics faster.
New algorithm solves uncertain Markov decision processes using Wasserstein uncertainty.
New algorithm clusters trajectories from multiple Markov chains with near-optimal error.
New neural processes use stacked Markov operators to improve flexibility.
This article considers a model for alternative processes for securities prices and compares this model with actual return data of several securities. The distributions of returns that appear in the model can be Gaussian as well as non-Gaussian; in particular they may have two peaks. We consider a discrete Markov chain …
Method reconstructs hidden Markov chains from insurance data.
Stochastic kernel based dimensionality reduction approaches have become popular in the last decade. The central component of many of these methods is a symmetric kernel that quantifies the vicinity between pairs of data points and a kernel-induced Markov chain on the data. Typically, the Markov chain is fully specified…
Representations based on random walks can exploit discrete data distributions for clustering and classification. We extend such representations from discrete to continuous distributions. Transition probabilities are now calculated using a diffusion equation with a diffusion coefficient that inversely depends on the dat…
Study best arm identification in restless Markov multi-armed bandits with state-dependent transitions.
Adapts flow matching for MCMC to improve sampling efficiency.
Study analyzes stock order transitions during US-China trade war using Markov chains.