Class Of Markov Chains Research Articles

Fuzzy hidden semi Markov chain with observation dependent states

This paper presents a mathematical model by incorporating the observation dependent property to the states of hidden semi Markov chain and then generalizing it in fuzzy space. In classical hidden semi Markov chain the states are not affected by observations. But the observation dependent property allows the states to be influenced by previous observations. Hidden semi Markov chain with states depending on observation provides a framework that facilitates the applications to model the durational structure of the hidden stochastic process and also gives the approval for the states to be influenced by previous observation. This model is framed on the fuzzy possibility space to cover a wide range of real life applications to make use of this model. This paper presents the derivation that provides the possible existence of hidden semi Markov chain with states depending on observation. Further it solves the evaluation problem of the developed model by giving modified algorithms.

Game Theoretic Interaction and Decision: A Quantum Analysis

An interaction system has a finite set of agents that interact pairwise, depending on the current state of the system. Symmetric decomposition of the matrix of interaction coefficients yields the representation of states by self-adjoint matrices and hence a spectral representation. As a result, cooperation systems, decision systems and quantum systems all become visible as manifestations of special interaction systems. The treatment of the theory is purely mathematical and does not require any special knowledge of physics. It is shown how standard notions in cooperative game theory arise naturally in this context. In particular, states of general interaction systems are seen to arise as linear superpositions of pure quantum states and Fourier transformation to become meaningful. Moreover, quantum games fall into this framework. Finally, a theory of Markov evolution of interaction states is presented that generalizes classical homogeneous Markov chains to the present context.

Equivalence of Szegedy’s and coined quantum walks

Szegedy's quantum walk is a quantization of a classical random walk or Markov chain, where the walk occurs on the edges of the bipartite double cover of the original graph. To search, one can simply quantize a Markov chain with absorbing vertices. Recently, Santos proposed two alternative search algorithms that instead utilize the sign-flip oracle in Grover's algorithm rather than absorbing vertices. In this paper, we show that these two algorithms are exactly equivalent to two algorithms involving coined quantum walks, which are walks on the vertices of the original graph with an internal degree of freedom. The first scheme is equivalent to a coined quantum walk with one walk-step per query of Grover's oracle, and the second is equivalent to a coined quantum walk with two walk-steps per query of Grover's oracle. These equivalences lie outside the previously known equivalence of Szegedy's quantum walk with absorbing vertices and the coined quantum walk with the negative identity operator as the coin for marked vertices, whose precise relationships we also investigate.

Hitting statistics from quantum jumps

We define the hitting time for a model of continuous-time open quantum walks in terms of quantum jumps. Our starting point is a master equation in Lindblad form, which can be taken as the quantum analogue of the rate equation for a classical continuous-time Markov chain. The quantum jump method is well known in the quantum optics community and has also been applied to simulate open quantum walks in discrete time. This method however, is well-suited to continuous-time problems. It is shown here that a continuous-time hitting problem is amenable to analysis via quantum jumps: The hitting time can be defined as the time of the first jump. Using this fact, we derive the distribution of hitting times and explicit exressions for its statistical moments. Simple examples are considered to illustrate the final results. We then show that the hitting statistics obtained via quantum jumps is consistent with a previous definition for a measured walk in discrete time [Phys. Rev. A 73, 032341 (2006)] (when generalised to allow for non-unitary evolution and in the limit of small time steps). A caveat of the quantum-jump approach is that it relies on the final state (the state which we want to hit) to share only incoherent edges with other vertices in the graph. We propose a simple remedy to restore the applicability of quantum jumps when this is not the case and show that the hitting-time statistics will again converge to that obtained from the measured discrete walk in appropriate limits.

Central Limit Theorem and Large Deviation Principle for Continuous Time Open Quantum Walks

Open Quantum Walks (OQWs), originally introduced by S. Attal, are quantum generalizations of classical Markov chains. Recently, natural continuous time models of OQW have been developed by C. Pellegrini. These models, called Continuous Time Open Quantum Walks (CTOQWs), appear as natural continuous time limits of discrete time OQWs. In particular they are quantum extensions of continuous time Markov chains. This article is devoted to the study of homogeneous CTOQW on $\mathbb{Z}^d$. We focus namely on their associated quantum trajectories which allow us to prove a Central Limit Theorem for the "position" of the walker as well as a Large Deviation Principle.

Efficient quantum circuits for Szegedy quantum walks

A major advantage in using Szegedy’s formalism over discrete-time and continuous-time quantum walks lies in its ability to define a unitary quantum walk by quantizing a Markov chain on a directed or weighted graph. In this paper, we present a general scheme to construct efficient quantum circuits for Szegedy quantum walks that correspond to classical Markov chains possessing transformational symmetry in the columns of the transition matrix. In particular, the transformational symmetry criteria do not necessarily depend on the sparsity of the transition matrix, so this scheme can be applied to non-sparse Markov chains. Two classes of Markov chains that are amenable to this construction are cyclic permutations and complete bipartite graphs, for which we provide explicit efficient quantum circuit implementations. We also prove that our scheme can be applied to Markov chains formed by a tensor product. We also briefly discuss the implementation of Markov chains based on weighted interdependent networks. In addition, we apply this scheme to construct efficient quantum circuits simulating the Szegedy walks used in the quantum Pagerank algorithm for some classes of non-trivial graphs, providing a necessary tool for experimental demonstration of the quantum Pagerank algorithm.

Forecasting closing returns of Borsa Istanbul Index with Markov Chain Process of fuzzy states

Purpose- The estimation regarding to the exact daily price of the stock market index has always been a difficult task in the business sector. Therefore, there are numerous research studies carried out to predict the direction of stock price index movement. Methodology- Classical Markov chain model (MC) is commonly used for this prediction and it gives valuable signals about the movements of the closing returns of the stock market index . In this paper, we propose Markov Chain Model with Fuzzy States (MCFS) to predict the closing returns of Borsa Istanbul (BIST 100) index using triangular fuzzy numbers. We apply this method to hold the information while system moves between the extreme values of the states. Findings- With this study, we show that the use of MCFS for the selected period provides a higher forecasting accuracy to the investors compared to MC model. Conclusion- Markov chains of the fuzzy states defines a stochastic system more precisely than the classical Markov chains and it gives more sensitive future prediction opportunities. It can be used for estimating returns of individual common stocks and also for the other investment instruments.

Passage Times, Exit Times and Dirichlet Problems for Open Quantum Walks

We consider open quantum walks on a graph, and consider the random variables defined as the passage time and number of visits to a given point of the graph. We study in particular the probability that the passage time is finite, the expectation of that passage time, and the expectation of the number of visits, and discuss the notion of recurrence for open quantum walks. We also study exit times and exit probabilities from a finite domain, and use them to solve Dirichlet problems and to determine harmonic measures. We consider in particular the case of irreducible open quantum walks. The results we obtain extend those for classical Markov chains.

Application of markov chains-fuzzy states to gold price

The estimation regarding to the return rate of gold prices has been one of the most popular topic for the financial area due to its high return. Therefore, several research studies were carried out to predict the direction of the gold price movement. The aim of this research is to determine the probabilistic transition matrix of the closing returns of gold prices using Markov Chain Model of Fuzzy States (MCFS) with triangular fuzzy numbers. We used this method in order to consider the information while system moves between the extreme values of the states which are more realistic and flexible than the classical Markov Chain technique. With this study, we can say that the use of MCFS can give valuable information to the investors about the gold price movements.

Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling

Bayesian analysis of continuous time, discrete state space time series is an important and challenging problem, where incomplete observation and large parameter sets call for user-defined priors based on known properties of the process. Generalized linear models have a largely unexplored potential to construct such prior distributions. We show that an important challenge with Bayesian generalized linear modelling of continuous time Markov chains is that classical Markov chain Monte Carlo techniques are too ineffective to be practical in that setup. We address this issue using an auxiliary variable construction combined with an adaptive Hamiltonian Monte Carlo algorithm. This sampling algorithm and model make it efficient both in terms of computation and analyst’s time to construct stochastic processes informed by prior knowledge, such as known properties of the states of the process. We demonstrate the flexibility and scalability of our framework using synthetic and real phylogenetic protein data, where a prior based on amino acid physicochemical properties is constructed to obtain accurate rate matrix estimates.

A quantum algorithm for uniform sampling of models of propositional logic based on quantum probability

We describe a class of quantum algorithms to generate models of propositional logic with equal probability. We consider quantum stochastic flows that are the quantum analogues of classical Markov chains and establish a relation between fixed points on the two flows. We construct chains inspired by von Neumann algorithms using uniform measures as fixed points to construct the corresponding irreversible quantum stochastic flows. We formulate sampling models of propositions in the framework of adiabatic quantum computing and solve the underlying satisfiability instances. Satisfiability formulation is an important and successful technique in modeling the decision theoretic problems in a classical context. We discuss some features of the proposed algorithms tested on an existing quantum annealer D-Wave II extending the simulation of decision theoretic problems to a quantum context.

Generalized Kac lemma for recurrence time in iterated open quantum systems

We consider recurrence to the initial state after repeated actions of a quantum channel. After each iteration a projective measurement is applied to check recurrence. The corresponding return time is known to be an integer for the special case of unital channels, including unitary channels. We prove that for a more general class of quantum channels the expected return time can be given as the inverse of the weight of the initial state in the steady state. This statement is a generalization of the Kac lemma for classical Markov chains.

Hamiltonian Monte Carlo and Borrowing Strength in Hierarchical Inverse Problems

AbstractBayesian approaches to uncertainty quantification and information acquisition in hierarchically defined inverse problems are presented. The techniques comprise simple updating, staged estimation, and multilevel model calibration. In particular, the estimation of material properties within an ensemble of identically manufactured structural elements is considered. It is shown how inferring the characteristics of an individual specimen can be accomplished by exhausting statistical strength from tests of other ensemble members. This is useful in experimental situations where evidence is scarce or unequally distributed. Hamiltonian Monte Carlo is proposed to cope with the numerical challenges of the devised approaches. The performance of the algorithm is studied and compared to classical Markov chain Monte Carlo sampling. It turns out that Bayesian posterior computations can be drastically accelerated.

Two Approaches to Obtain the Strong Converse Exponent of Quantum Hypothesis Testing for General Sequences of Quantum States

We present two general approaches to obtain the strong converse rate of quantum hypothesis testing for correlated quantum states. One approach requires that the states satisfy a certain factorization property; typical examples of such states are the temperature states of translation-invariant finite-range interactions on a spin chain. The other approach requires the differentiability of a regularized R\'enyi $\alpha$-divergence in the parameter $\alpha$; typical examples of such states include temperature states of non-interacting fermionic lattice systems, and classical irreducible Markov chains. In all cases, we get that the strong converse exponent is equal to the Hoeffding anti-divergence, which in turn is obtained from the regularized R\'enyi divergences of the two states.

Entropy, Information Theory, Information Geometry and Bayesian Inference in Data, Signal and Image Processing and Inverse Problems

The main content of this review article is first to review the main inference tools using Bayes rule, the maximum entropy principle (MEP), information theory, relative entropy and the Kullback–Leibler (KL) divergence, Fisher information and its corresponding geometries. For each of these tools, the precise context of their use is described. The second part of the paper is focused on the ways these tools have been used in data, signal and image processing and in the inverse problems, which arise in different physical sciences and engineering applications. A few examples of the applications are described: entropy in independent components analysis (ICA) and in blind source separation, Fisher information in data model selection, different maximum entropy-based methods in time series spectral estimation and in linear inverse problems and, finally, the Bayesian inference for general inverse problems. Some original materials concerning the approximate Bayesian computation (ABC) and, in particular, the variational Bayesian approximation (VBA) methods are also presented. VBA is used for proposing an alternative Bayesian computational tool to the classical Markov chain Monte Carlo (MCMC) methods. We will also see that VBA englobes joint maximum a posteriori (MAP), as well as the different expectation-maximization (EM) algorithms as particular cases.

Strong Stationary Duality for Diffusion Processes

We develop the theory of strong stationary duality for diffusion processes on compact intervals. We analytically derive the generator and boundary behavior of the dual process and recover a central tenet of the classical Markov chain theory in the diffusion setting by linking the separation distance in the primal diffusion to the absorption time in the dual diffusion. We also exhibit our strong stationary dual as the natural limiting process of the strong stationary dual sequence of a well-chosen sequence of approximating birth-and-death Markov chains, allowing for simultaneous numerical simulations of our primal and dual diffusion processes. Lastly, we show how our new definition of diffusion duality allows the spectral theory of cutoff phenomena to extend naturally from birth-and-death Markov chains to the present diffusion context.

Quantized recurrence time in unital iterated open quantum dynamics

The expected return time to the original state is a key concept characterizing systems obeying both classical or quantum dynamics. We consider iterated open quantum dynamical systems in finite dimensional Hilbert spaces, a broad class of systems that includes classical Markov chains and unitary discrete time quantum walks on networks. Starting from a pure state, the time evolution is induced by repeated applications of a general quantum channel, in each timestep followed by a measurement to detect whether the system has returned to the original state. We prove that if the superoperator is unital in the relevant Hilbert space (the part of the Hilbert space explored by the system), then the expectation value of the return time is an integer, equal to the dimension of this relevant Hilbert space. We illustrate our results on partially coherent quantum walks on finite graphs. Our work connects the previously known quantization of the expected return time for bistochastic Markov chains and for unitary quantum walks, and shows that these are special cases of a more general statement. The expected return time is thus a quantitative measure of the size of the part of the Hilbert space available to the system when the dynamics is started from a certain state.

TAU-REX I: A NEXT GENERATION RETRIEVAL CODE FOR EXOPLANETARY ATMOSPHERES

Spectroscopy of exoplanetary atmospheres has become a well established method for the characterisation of extrasolar planets. We here present a novel inverse retrieval code for exoplanetary atmospheres. TauRex (Tau Retrieval for Exoplanets) is a line-by-line radiative transfer fully Bayesian retrieval framework. TauRex includes the following features: 1) the optimised use of molecular line-lists from the Exomol project; 2) an unbiased atmospheric composition prior selection, through custom built pattern recognition software; 3) the use of two independent algorithms to fully sample the Bayesian likelihood space: nested sampling as well as a more classical Markov Chain Monte Carlo approach; 4) iterative Bayesian parameter and model selection using the full Bayesian Evidence as well as the Savage-Dickey Ratio for nested models, and 5) the ability to fully map very large parameter spaces through optimal code parallelisation and scalability to cluster computing. In this publication we outline the TauRex framework and demonstrate, using a theoretical hot-Jupiter transmission spectrum, the parameter retrieval and model selection. We investigate the impact of Signal-to-Noise and spectral resolution on the retrievability of individual model parameters, both in terms of error bars on the temperature and molecular mixing ratios as well as its effect on the model's global Bayesian evidence.

Noisy quantum cellular automata for quantum versus classical excitation transfer.

We introduce a class of noisy quantum cellular automata on a qubit lattice that includes all classical Markov chains, as well as maps where quantum coherence between sites is allowed to build up over time. We apply such a construction to the problem of excitation transfer through 1D lattices, and compare the performance of classical and quantum dynamics with equal local transition probabilities. Our discrete approach has the merits of stripping down the complications of the open system dynamics, of clearly isolating coherent effects, and of allowing for an exact treatment of conditional dynamics, all while capturing a rich variety of dynamical behaviors.

Self-Excited Threshold Poisson Autoregression

This article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. Generalized from the Poisson autoregression, it allows more flexible, and even negative correlation, in the observations, which cannot be produced by the single-regime model. Classical Markov chain theory and Lyapunov’s method are used to derive the conditions under which the process has a unique invariant probability measure and to show a strong law of large numbers of the intensity process. Moreover, the asymptotic theory of the maximum likelihood estimates of the parameters is established. A simulation study and a real-data application are considered, where the model is applied to the number of major earthquakes in the world. Supplementary materials for this article are available online.