New Bernstein and Hoeffding type inequalities for regenerative Markov chains

We give the estimates of approximation errors while approximating of a positive operator $A$ in a Banach space by analytic vectors. Our main results are formulated in the form of Bernstein and Jackson type inequalities with explicitly calculated constants. We consider the classes of invariant subspaces ${\mathcal E}_{q,p}^{\nu,\alpha}(A)$ of analytic vectors of $A$ and the special scale of approximation spaces $\mathcal {B}_{q,p,\tau}^{s,\alpha}(A)$ associated with the complex degrees of positive operator. The approximation spaces are determined by $E$-functional, that plays a similar role as the module of smoothness. We show that the approximation spaces can be considered as interpolation spaces generated by $K$-method of real interpolation. The constants in the Bernstein and Jackson type inequalities are expressed using the normalization factor.

In this work, we discuss generalizations of the classical Bernstein and\nMarkov type inequalities for polynomials and we present some new inequalities\nfor the $k$th Fr\\'echet derivative of homogeneous polynomials on real and\ncomplex $L_{p}(\\mu)$ spaces. We also give applications to homogeneous\npolynomials and symmetric multilinear mappings in $L_{p}(\\mu)$ spaces. Finally,\nBernstein's inequality for homogeneous polynomials on both real and complex\nHilbert spaces has been discussed.\n

We prove the right Lax-type inequality on subarcs of the unit circle of the complex plane for complex algebraic polynomials of degree n having no zeros in the open unit disk. This is done by establishing the right Bernstein–Szegő–Videnskii type inequality for real trigonometric polynomials of degree at most n on intervals shorter than the period. The paper is closely related to recent work by B. Nagy and V. Totik. In fact, their asymptotically sharp Bernstein-type inequality for complex algebraic polynomials of degree at most n on subarcs of the unit circle is recaptured by using more elementary methods. Our discussion offers a somewhat new way to see V.S. Videnskii’s Bernstein and Markov type inequalities for trigonometric polynomials of degree at most n on intervals shorter than a period, two classical polynomial inequalities first published in 1960. A new Riesz–Schur type inequality for trigonometric polynomials is also established. Combining this with Videnskii’s Bernstein-type inequality gives Videnskii’s Markov-type inequality immediately.

The most complex models with the high quality of the results obtained are, as a rule, more expensive in terms of developer qualifications and computational resources. During process design, a detailed description of the object is often not required, and the accuracy of the results obtained should not be higher than the accuracy of the measuring instruments used. Thus, the optimal combination of simplicity and quality of the mathematical description of technological processes is an urgent task of mathematical modeling. To identify a cell model developed based on the theory of Markov chains, data obtained by solving discrete models of the Boltzmann equation are used. A method to identify cell models of a fluidized bed reactor has been developed using data obtained based on solving discrete models of the Boltzmann equation. The adequacy of the identified model of a fluidized bed reactor has been verified. An approach to develop computational support for a cell model based on the theory of Markov chains is presented. The analysis of the results obtained has shown an adequate description of the processes in fluidized bed reactors in terms of cell models. The models are developed based on the theory of Markov chains and identified based on the results obtained within the framework of discrete models of the Boltzmann equation. The proposed method to identify and verify cell models provides the possibility to obtain simultaneously acceptable indicators of model simplicity and the accuracy of calculation of the design and operating parameters of fluidized bed reactors.

The goal of this paper is to develop a general method to establish conditional ergodicity of infinite-dimensional Markov chains. Given a Markov chain in a product space, we aim to understand the ergodic properties of its conditional distributions given one of the components. Such questions play a fundamental role in the ergodic theory of nonlinear filters. In the setting of Harris chains, conditional ergodicity has been established under general nondegeneracy assumptions. Unfortunately, Markov chains in infinite-dimensional state spaces are rarely amenable to the classical theory of Harris chains due to the singularity of their transition probabilities, while topological and functional methods that have been developed in the ergodic theory of infinite-dimensional Markov chains are not well suited to the investigation of conditional distributions. We must therefore develop new measure-theoretic tools in the ergodic theory of Markov chains that enable the investigation of conditional ergodicity for infinite dimensional or weak-* ergodic processes. To this end, we first develop local counterparts of zero-two laws that arise in the theory of Harris chains. These results give rise to ergodic theorems for Markov chains that admit asymptotic couplings or that are locally mixing in the sense of H. F\"{o}llmer, and to a non-Markovian ergodic theorem for stationary absolutely regular sequences. We proceed to show that local ergodicity is inherited by conditioning on a nondegenerate observation process. This is used to prove stability and unique ergodicity of the nonlinear filter. Finally, we show that our abstract results can be applied to infinite-dimensional Markov processes that arise in several settings, including dissipative stochastic partial differential equations, stochastic spin systems and stochastic differential delay equations.

We prove a new Bernstein type inequality in L p L^p spaces associated with the normal and the tangential derivatives on the boundary of a general compact C 2 C^2 -domain. We give two applications: Marcinkiewicz type inequality for discretization of L p L^p norms and positive cubature formulas. Both results are optimal in the sense that the number of function samples used has the order of the dimension of the corresponding space of algebraic polynomials.

In many applications, successive observations of a process, say X 1, X 2,…, have an inherent time component associated with them. For example, the X i could be the state of the weather at a particular location on the i th day, counting from some fixed day. In a simplistic model, the state of the weather could be “dry” or “wet,” quantified as, say, 0 and 1. It is hard to believe that in such an example, the sequence X 1, X 2,… could be mutually independent. The question then arises how to model the dependence among the X i ′s. Probabilists have numerous dependency models. A particular model that has earned a very special status is called the Markov chain. In a Markov chain model, we assume that the future, given the entire past and the present state of a process, depends only on the present state. In the weather example, suppose we want to assign a probability that tomorrow, say March 10, will be dry, and suppose that we have available to us the precipitation history for each day from January 1 to March 9. In a Markov chain model, our probability that March 10 will be dry will depend only on the state of the weather on March 9, even though the entire past precipitation history was available to us. As simple as it sounds, Markov chains are enormously useful in applications, perhaps more than any other specific dependency model. They also are independently relevant to statistical computing in very important ways. The topic has an incredibly rich and well-developed theory, with links to many other topics in probability theory. Familiarity with basic Markov chain terminology and theory is often considered essential for anyone interested in studying statistics and probability.We present an introduction to basic Markov chain theory in this chapter.KeywordsMarkov ChainStationary DistributionMarkov Chain ModelTransition Probability MatrixStationary Markov ChainThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this work we develop a bootstrap method based on the theory of Markov chains. The method moves from the two competing objectives that a researcher pursues when performing a bootstrap procedure: (i) to preserve the structural similarity – in statistical sense – between the original and the bootstrapped sample; (ii) to assure a diversification of the latter with respect to the former. The original sample is assumed to be driven by a Markov chain. The approach we follow is to implement an optimization problem to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. The basic ingredients of the model are the transition probabilities, whose distance is measured through a suitably defined functional. We apply the method to the series of electricity prices in Spain. A comparison with the Variable Length Markov Chain bootstrap, which is a well established bootstrap method, shows the superiority of our proposal in reproducing the dependence among data.KeywordsMarkov ChainInformation LossElectricity PriceBootstrap ProcedureTransition Probability MatrixThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

An action function for a higher step Grushin operator

New and sharp estimates are derived for the growth in the complex plane of polynomials known to have a curved majorant on a given ellipse. These so-called Bernstein type inequalities are closely connected with certain constrained Chebyshev approximation problems on ellipses. Also presented are some new results for approximation problems of this type.

Bernstein type inequalities for restrictions of polynomials to complex submanifolds of [formula omitted

One of the central considerations in the theory of Markov chains is their convergence to an equilibrium. Coefficients of ergodicity provide an efficient method for such an analysis. Besides giving sufficient and sometimes necessary conditions for convergence, they additionally measure its rate. In this paper we explore coefficients of ergodicity for the case of imprecise Markov chains. The latter provide a convenient way of modelling dynamical systems where parameters are not determined precisely. In such cases a tool for measuring the rate of convergence is even more important than in the case of precisely determined Markov chains, since most of the existing methods of estimating the limit distributions are iterative. We define a new coefficient of ergodicity that provides necessary and sufficient conditions for convergence of the most commonly used class of imprecise Markov chains. This so-called weak coefficient of ergodicity is defined through an endowment of the structure of a metric space to the class of imprecise probabilities. Therefore we first make a detailed analysis of the metric properties of imprecise probabilities.

SummaryConsider a system of n players in which each initially starts on a different team. At each time step, we select an individual winner and an individual loser randomly and the loser joins the winner's team. The resulting Markov chain and stochastic matrix clearly have one absorbing state, in which all players are on the same team, but the combinatorics along the way are surprisingly elegant. The expected number of time steps until each team is eliminated is a ratio of binomial coefficients. When a team is eliminated, the probabilities that the players are configured in various partitions of n into t teams are given by multinomial coefficients. The expected value of the time to absorbtion is (n - 1)2 steps. The results depend on elementary combinatorics, linear algebra, and the theory of Markov chains.

The aim of our research is to contribute to the modeling of relative humidity with Markov chains and its use in crop protection forecasting in the Tokaj-Hegyalja wine region. The two most common wine grape varieties in this region are Vitis vinifera cv. Furmint and V. vinifera cv. Hárslevelű. For noble rot on these varieties, humidity in the optimum range is an essential environmental condition. Using 10-min and 60-min resolution humidity data sets from the Hungarian Meteorological Service (HungaroMet) for the period 2002−2020, we estimated the probability that a random humidity measurement will fall into a given result (five humidity categories) in the future. The mathematical model used for this research is the theory of Markov chains, a new approach to statistical analysis of relative humidity data. It is pointed out that the Markov process of relative humidity variation and the results of a 60-min resolution data series across 19 years of study can be used to model humidity.

We have suggested that sine-wave based acuity gratings (such as Gabor functions) should be replaced by a new set of test functions - functions which act as the eigenfunctions of an appropriate operator (Stewart & Pinkham, in press). The Hermite functions emerge as an important special case, and have recently been applied to problems in image analysis (Martens, 1990). The Hermite functions are generated as the eigenfunctions of a differential operator which produces a simple and appealing mathematical model of space-variant acuity. The operator’s mathematical properties are in better agreement with the spatial inhomogeneity of the visual field than is the Fourier kernel. Surprisingly, these functions are their own Fourier transformations, and so are easily related to the Fourier transforms which are used in image analysis. If we are correct, human factors work on visual acuity can be placed on a coherent mathematical foundation, and new approaches to evaluating the quality of visual images will emerge. Just as important, a space-variant model of human visual acuity includes as a special case, the space-invariant models of image analysis used in engineering studies of image processing. The Hermite decomposition of the stimulus is directly related to the eigenfunctions of our space-variant acuity operator. The theory of Markov chains can be tied directly to the components of such a Hermite decomposition. The Hermite breakdown of two-dimensional and one-dimensional fractional Brownian motion produces components having corresponding spectra. Consequently, the information inherent in two- dimensional fractional Brownian motion is preserved under the Hermite decomposition, and finds a natural expression as a Markov process. Markov models are widely used for automatic speech recognition. Similar methods can be used to capture the inherent structure of visual images. Some natural scenes can be represented by fractals. Many images can be treated as