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The paper refers to the study of properties of solutions to two-parameter stochastic inclusions and set-valued stochastic equations with set-valued mixed integrals driven by finite variation processes and martingales. We present new types of such inclusions and equations that generalize those studied earlier. Apart from existence results to such inclusions and equations also topological properties of their solutions are studied. Additionally some connections between their solutions are established. The results obtained in the paper present a set-valued counterpart dealing with this topic known both in deterministic and stochastic cases. [ABSTRACT FROM AUTHOR]

The transition kernel of an ℝn-valued diffusion or jump diffusion process {Xt} is known to satisfy the Feller property if {Xt} is the solution of an SDE whose coefficients are Lipschitz continuous. This Lipschitz route to Feller falls short if {Xt} is the solution of an SDE whose coefficients depend on a state-dependent regime-switching process {θt}. In this paper it is shown that pathwise uniqueness and the Feller property are satisfied under mild conditions for a regime-switching jump diffusion process {Xt, θt} with hybrid jumps, i.e. jumps in {Xt} that occur simultaneously with {θt} switching. [ABSTRACT FROM AUTHOR]

In this paper, we present the stability result of a spatial semi-discrete scheme to backward stochastic differential equations taking values in a Hilbert space. Under suitable assumptions of the final value and the drift, a convergence rate is established. [ABSTRACT FROM AUTHOR]

In this paper we provide conditions under which the hitting-time problem for Brownian motion is equivalent to solving a heat equation with moving boundary and distributional initial conditions. Motivated by the hitting-time problem, we study the heat equation with absorbing moving boundaries. Using Fourier analysis we develop a procedure to solve this problem for a family of curves that includes the square root, quadratic, and cubic boundaries. As an application of our results, and using Sturm-Liouvile theory, we compute the density of the hitting time of a Brownian motion to a family of quadratic boundaries. [ABSTRACT FROM AUTHOR]

In the first part of this paper, we establish the well-posedness for solutions of Caputo stochastic fractional differential equations (for short Caputo SFDE) of order α ∈ (1 2 , 1) in Lp spaces with p ≥ 2 whose coefficients satisfy a standard Lipschitz condition. More precisely, we first show a result on the existence and uniqueness of solutions, next we show the continuous dependence of solutions on the initial values and on the fractional exponent α. The second part of this paper is devoted to studying the regularity in time for solutions of Caputo SFDE. As a consequence, we obtain that a solution of Caputo SFDE has a δ-Hölder continuous version for any δ ∈ (0 , α − 1 2). The main ingredient in the proof is to use a temporally weighted norm and the Burkholder-Davis-Gundy inequality. [ABSTRACT FROM AUTHOR]

This paper is concerned with the dynamics of stochastic lattice systems with Markovian switching. Based on the well-posedness of solutions, we first prove the ergodicity of invariant measures and show that the Markov chain facilitates the existence of invariant measures. In order to investigate numerical invariant measures, the convergence of invariant measures is considered between the original systems and their finite-dimensional truncated systems. Due to this, we can use the backward Euler-Maruyama method to approximate invariant measures for such infinite-dimensional systems. This work provides a feasible path for the convergence of the finite-dimensional numerical invariant measures to the analytical invariant measure. [ABSTRACT FROM AUTHOR]

We study multidimensional stochastic volatility models in which the volatility process is a positive continuous function of a continuous multidimensional Volterra process that can be not self-similar. The main results obtained in this paper are a generalization of the results due, in the one-dimensional case, to Cellupica and Pacchiarotti (J. Theor. Probab. 34(2):682–727). We state some (pathwise and finite-dimensional) large deviation principles for the scaled log-price and as a consequence some (pathwise and finite-dimensional) short-time large deviation principles. [ABSTRACT FROM AUTHOR]

Contributions of the present paper consist of two parts. In the first one, we contribute to the theory of stochastic calculus for signed measures. For instance, we provide some results permitting to characterize martingales and Brownian motion both defined under a signed measure. We also prove that the uniformly integrable martingales (defined with respect to a signed measure) can be expressed as relative martingales and we provide some new results to the study of the class Σ (H) . The second part is devoted to the construction of solutions for the homogeneous skew Brownian motion equation and for the inhomogeneous skew Brownian motion equation. To do this, our ingredients are the techniques and results developed in the first part that we apply on some stochastic processes borrowed from the theory of stochastic calculus for signed measures. Our methods are inspired by those used by Bouhadou and Ouknine in [2013]. Moreover, their solution of the inhomogeneous skew Brownian motion equation is a particular case of those we propose in this paper. [ABSTRACT FROM AUTHOR]

In this paper we deal with some open problems concerned with Gamma subordinators. In particular, we first provide a representation for the moments of the inverse gamma subordinator. Then, we focus on λ-potentials and we study the governing equations associated with Gamma subordinators and inverse processes. Such representations are given in terms of higher transcendental functions, also known as Volterra functions. [ABSTRACT FROM AUTHOR]

In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index. We preprocess the index data based on fuzzy theory. After that, S&P 500 stock index data for the past 10 years are analyzed, and a deterministic parameter is extracted using various machine and deep learning methods. The results show that the new model, where fuzzy parameters are incorporated, can incorporate the long-term dependence in the classical Barndorff-Nielsen and Shephard model. The modification is based on only a few changes compared to the classical model. At the same time, the resulting analysis effectively captures the stochastic dynamics of the stock index time series. [ABSTRACT FROM AUTHOR]

In this paper, we investigate an existence of minimal (resp. maximal) solutions for backward doubly stochastic differential equations (BDSDEs, in short) when the coefficient with respect to the forward integral is left (resp. right)-continuous in y and continuous in z with stochastic linear growth in (y, z). Also, the associated comparison theorem is obtained. [ABSTRACT FROM AUTHOR]

In this article, we consider the filtering problem for partially observed diffusions, which are regularly observed at discrete times. We are concerned with the case when one must resort to time-discretization of the diffusion process if the transition density is not available in an appropriate form. In such cases, one must resort to advanced numerical algorithms such as particle filters to consistently estimate the filter. It is also well known that the particle filter can be enhanced by considering hierarchies of discretizations and the multilevel Monte Carlo (MLMC) method, in the sense of reducing the computational effort to achieve a given mean square error (MSE). A variety of multilevel particle filters (MLPF) have been suggested in the literature, e.g., in Jasra et al., SIAM J, Numer. Anal., 55, 3068–3096. Here we introduce a new alternative that involves a resampling step based on the optimal Wasserstein coupling. We prove a central limit theorem (CLT) for the new method. On considering the asymptotic variance, we establish that in some scenarios, there is a reduction, relative to the approach in the aforementioned paper by Jasra et al., in computational effort to achieve a given MSE. These findings are confirmed in numerical examples. We also consider filtering diffusions with unstable dynamics; we empirically show that in such cases a change of measure technique seems to be required to maintain our findings. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of social media addiction that pose a major threat to the human population especially children and teenagers. It is well known that Cognitive Behavioral Therapy (CBT) is an effective treatment to treat the addict individuals and delay in the treatment leads the patient to worst stage even to death. Therefore, it is important to identify the individuals who has addiction symptoms at early stage and to provide proper counseling. We propose and analyze a nonlinear mathematical model for social media addiction problem using case detection strategy to reduce the addiction. The basic reproduction number and equilibria of the model are computed. Further, the deterministic model is extended to delay differential equation model by incorporating transmission delay and treatment delay in the system. The local stability of different equilibria is discussed in detail. Additionally, the model is converted to stochastic model and numerical simulation is carried out to compare the results of both deterministic and stochastic model. Numerical result shows that the introduction of time delays can destabilize the model system and Hopf bifurcation occurs due to periodic oscillations when certain equilibrium point crosses the delay threshold limit. Our results of stochastic model show a smaller number of social media users and addict population when compared with deterministic model. Also, our results reveal that detection and counseling parameters play a vital role in reducing addiction population. Presented results clearly suggest that there is a need to use effective detection strategy and suitable counseling program to reduce the social media addiction level. [ABSTRACT FROM AUTHOR]

In this paper, the approximate controllability of Hilfer fractional stochastic evolution inclusion with nonlocal conditions is investigated. By using fractional calculus, semigroups theory, stochastic analysis and the fixed point theorem for multi-valued maps, a new sufficient condition for the existence of mild solution of the Hilfer fractional stochastic system in the space of weighted continuous functions is derived. Furthermore, a novel sufficient condition for the approximate controllability of the Hilfer fractional stochastic evolution inclusion with nonlocal conditions is established. Finally, an example is presented to illustrate the main results. [ABSTRACT FROM AUTHOR]

The problem of investigating the continuity in the Hurst index arises naturally in statistical inferences related to fractional Brownian motion. In this paper, based on the techniques of the Malliavin calculus, we introduce a method to deal with this problem. We first provide an explicit bound on the difference between two non-smooth functionals of Malliavin differentiable random variables. Then, we apply the obtained bound to show Lipchitz continuity of fractional stochastic Volterra integro-differential equations and its additive functionals. [ABSTRACT FROM AUTHOR]

In this paper, we first study the well-posedness of a class of McKean-Vlasov stochastic partial differential equations driven by cylindrical α-stable process, where α ∈ (1 , 2). Then by the method of the Khasminskii's time discretization, we prove the averaging principle of a class of multiscale McKean-Vlasov stochastic partial differential equations driven by cylindrical α-stable processes. Meanwhile, we obtain a specific strong convergence rate. [ABSTRACT FROM AUTHOR]

Regularity estimates for an integral operator with a symmetric continuous kernel on a convex bounded domain are derived. The covariance of a mean-square continuous random field on the domain is an example of such an operator. The estimates are of the form of Hilbert–Schmidt norms of the integral operator and its square root, composed with fractional powers of an elliptic operator equipped with homogeneous boundary conditions of either Dirichlet or Neumann type. These types of estimates, which couple the regularity of the driving noise with the properties of the differential operator, have important implications for stochastic partial differential equations on bounded domains as well as their numerical approximations. The main tools used to derive the estimates are properties of reproducing kernel Hilbert spaces of functions on bounded domains along with Hilbert–Schmidt embeddings of Sobolev spaces. Both non-homogeneous and homogeneous kernels are considered. In the latter case, results in a general Schatten class norm are also provided. Important examples of homogeneous kernels covered by the results of the paper include the class of Matérn kernels. [ABSTRACT FROM AUTHOR]

As COVID-19 is spreading, national agencies need to monitor and track several metrics. Since we do not have perfect testing programs on the hand, one needs to develop an advanced sampling strategies for prevalence study, control and management. Here we introduce REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management and control and justify its usage for COVID-19. We show its advantages over classical massive individual testing sampling plans. We also point out how regional and spatial heterogeneity underlines proper sampling. Fundamental importance of adaptive control parameters from emergency health stations and medical frontline is outlined. Since the Northern hemisphere entered Autumn and Winter season (this paper was originally submitted in November 2020), practical illustration from spatial heterogeneity of Chile (Southern hemisphere, which already experienced COVID-19 winter outbreak peak) is underlying the importance of proper regional heterogeneity of sampling plan. We explain the regional heterogeneity by microbiological backgrounds and link it to behavior of Lyapunov exponents. We also discuss screening by antigen tests from the perspective of "on the fly" biomarker validation, i.e., during the screening. [ABSTRACT FROM AUTHOR]

The paper suggests a way of stochastic integration of random integrands with respect to fractional Brownian motion with the Hurst parameter H > 1∕2. The integral is defined initially on the processes that are" piecewise" predictable on a short horizon. Then the integral is extended on a wide class of square integrable adapted random processes. This class is described via a mild restriction on the growth rate of the conditional mean square error for the forecast on an arbitrarily short horizon given current observations. On the other hand, a pathwise regularity, such as Hölder condition, etc., is not required for the integrand. The suggested integration can be interpreted as foresighted integration for integrands featuring certain restrictions on the forecasting error. This integration is based on Itô's integration and does not involve Malliavin calculus or Wick products. In addition, it is shown that these stochastic integrals depend right continuously on H at H = 1/2. [ABSTRACT FROM AUTHOR]

In this paper, we study asymptotic properties of the maximum likelihood estimator (MLE) for the speed of a stochastic wave equation. We follow a well-known spectral approach to write the solution as a Fourier series, then we project the solution to a N-finite dimensional space and find the estimator as a function of the time and N. We then show consistency of the MLE using classical stochastic analysis. Afterward, we prove the asymptotic normality using the Malliavin–Stein method. We also study asymptotic properties of a discretized version of the MLE for the parameter. We provide this asymptotic analysis of the proposed estimator as the number of Fourier modes, N, used in the estimation and the observation time go to infinity. Finally, we illustrate the theoretical results with some numerical experiments. [ABSTRACT FROM AUTHOR]

In the present paper we study the asymptotic behavior of the auto-covariance function for Ornstein–Uhlenbeck (OU) processes driven by Gaussian noises with stationary and non-stationary increments and for Hermite OU processes. Our results are generalizations of the corresponding results of Cheridito et al. and Kaarakka and Salminen. [ABSTRACT FROM AUTHOR]

This paper studied a stochastic epidemic model of the spread of the novel coronavirus (COVID-19). Severe factors impacting the disease transmission are presented by white noise and compensated Poisson noise with possibly infinite characteristic measure. Large time estimates are established based on Kunita's inequality rather than Burkholder-Davis-Gundy inequality for continuous diffusions. The effect of stochasticity is taken into account in the formulation of sufficient conditions for the extinction of COVID-19 and its persistence. Our results prove that environmental fluctuations can be privileged in controlling the pandemic behavior. Based on real parameter values, numerical results are presented to illustrate obtained results concerning the extinction and the persistence in mean of the disease. [ABSTRACT FROM AUTHOR]

This paper discusses the existence result for a class of non-instantaneous impulsive Hilfer fractional stochastic systems (NIHFSS) driven by mixed Brownian motion and L e ̂ vy noise. Sufficient conditions are obtained by utilizing M o ¨ nch fixed point theorem (FPT) for the existence of solution of non-instantaneous impulsive Hilfer fractional stochastic system of order 0 < γ < 1 and of type 0 ≤ δ ≤ 1. The existence result is derived by adapting stochastic analysis techniques, the measure of non-compactness, semigroup theory, and fractional calculus. The discussed theory is illustrated through an example. [ABSTRACT FROM AUTHOR]

The Adomian decomposition method (ADM) is a powerful tool for solving numerous nonlinear functional equations and a large class of initial/boundary value problems. The main task in the application of ADM is the computation of Adomian polynomials (APs). In addition to classic APs, Rach's nonclassic APs, called class I-IV polynomials, are used to solve a wide range of nonlinear functional equations. In this paper, we present probabilistic interpretations for the nonclassic APs, including class V polynomials studied by Duan. We derive the recurrence relations for the computation of nonclassic APs using our approach. Some numerical examples are discussed to show that the probabilistic approach to compute the nonclassic APs is attractive and is also simple. Finally, a probabilistic proof is given for the known fact that the class IV APs are the classic APs. The probabilistic approach offers an alternative method to the existing analytical or combinatorial approach. [ABSTRACT FROM AUTHOR]

In this paper, we analyze a stochastic multigroup DS-DI-A model for the transmission of HIV. We establish sufficient conditions for the existence and uniqueness of an ergodic stationary distribution of positive solutions to the system by constructing a series of suitable Lyapunov functions. Moreover, we make up adequate conditions for complete eradication and wiping out of the diseases. In a biological viewpoint, the existence of a stationary distribution shows that the diseases will be prevalent and persistent in the long term. [ABSTRACT FROM AUTHOR]

In this paper, the problem of the large deviations for the invariant measures of the multivalued stochastic differential equations is considered. Under the assumptions of diffusion coefficient being non-Lipschitz and elliptic, we establish the large deviation principle for the invariant measures of the solutions to the multivalued stochastic differential equations. The proof is based on the work of large deviations and invariant measures for the solutions to the multivalued stochastic differential equations. [ABSTRACT FROM AUTHOR]

In this paper, we study the Wong-Zakai approximations and long term behavior of the stochastic FitzHugh-Nagumo system driven by a white noise. We first prove the existence and uniqueness of tempered pullback attractors for the Wong-Zakai approximation system. Then we show that the attractors of Wong-Zakai approximations converges to the attractor of the stochastic FitzHugh-Nagumo system for both additive and multiplicative noise on unbounded domains. The tail estimates and decomposition method are employed to derive the pullback asymptotic compactness of solutions in order to overcome the obstacles caused by the non-compactness of Sobolev embeddings on unbounded domains as well as the absence of regularity of one component of the solutions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the periodic measures of the stochastic impulsive Hopfield-type lattice systems driven by nonlinear noise. By the properties of periodic Markov processes, the existence of periodic measures for the impulsive stochastic lattice systems is established. To this end, we employ the idea of uniform estimates on the tails of the solutions to show the tightness of a family of distributions of the solutions of the lattice systems. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the salient features of a stochastic multigroup SEI epidemic model. We obtain sufficient criteria for the existence and uniqueness of an ergodic stationary distribution of positive solutions to the system by establishing a series of suitable Lyapunov functions. In a biological viewpoint, the existence of a stationary distribution indicates that the diseases will be prevalent and persistent in the long term. In addition, we make up adequate conditions for complete eradication and wiping out of the diseases. Some numerical simulations are presented to illustrate our main results. [ABSTRACT FROM AUTHOR]

In this paper, we obtain a probabilistic relationship between the exponential Bell polynomials and the weighted sums of independent Poisson random variables. A recently established probabilistic connection between the Adomian polynomials and independent Poisson random variables can be derived from the obtained relationship. This result has importance because any known identity for the exponential Bell polynomials will generate a new identity for the Poisson random variables. We use the obtained relationship to derive several new identities for the joint distribution of weighted sums of independent Poisson random variables. Few examples are provided that substantiate the obtained identities. [ABSTRACT FROM AUTHOR]

The main task of this paper is to model the dependency and effects of the Lehman Brothers financial collapse event using a superposed and coupled Ornstein-Uhlenbeck type system of stochastic differential equations driven by a Lévy process. The development of these types of efficient models to correctly quantify and predict the sample paths of these kinds of time series is essential since it helps prevent losses or maximize profits in the field of financial modeling. The results obtained from this study suggest that the solutions of the stochastic models provide a good fit to the high frequency financial stock market data since it captures realistic dependence structures. In addition, the estimated model parameters are useful for making inferences and predicting these types of events. [ABSTRACT FROM AUTHOR]

In the paper we consider the problem of estimating parameters entering the drift of a fractional Ornstein-Uhlenbeck type process in the non-ergodic case, when the underlying stochastic integral is of Young type. We consider the sampling scheme that the process is observed continuously on [ 0 , T ] and T → ∞. For known Hurst parameter H ∈ (0.5 , 1) , i.e. the long range dependent case, we construct a least-squares type estimator and establish strong consistency. Furthermore, we prove a second order limit theorem which provides asymptotic normality for the parameters of the periodic function with a rate depending on H and a non-central Cauchy limit result for the mean reverting parameter with exponential rate. For the special case that the periodicity parameter is the weight of a periodic function, which integrates to zero over the period, we can even improve the rate to T. [ABSTRACT FROM AUTHOR]

The main task of this paper is to provide sufficient conditions for the stabilizability of nonlinear stochastic differential systems which are not necessarily affine in the control. This result extends to non affine stochastic differential systems the well–known theorem of Jurdjevic-Quinn and incorporates an earlier version of the stochastic Jurdjevic-Quinn theorem proved by the author. [ABSTRACT FROM AUTHOR]

This paper includes investigation of approximate controllability results for a multi-term time-fractional stochastic differential system in a Hilbert space. A new set of results is obtained with non-Lipschitz conditions of nonlinear functions using the well-known Picard type iterations. The basic tools of fractional calculus, generalization of semigroup, and stochastic analysis techniques have been utilized. Finally, an application is discussed to illustrate the developed theory. [ABSTRACT FROM AUTHOR]

In this paper we consider inhomogeneous time change equations (TCE) for Markov chains. We prove the existence and uniqueness of solution of the TCE and then study the influence of the time change on Markov consistency property and Markov structures of processes. In the first part we focus on time-changed Markov chains, whereas in the second part we use the TCE to change time in diffusion processes, obtaining regime-switching diffusions. This procedure is useful for applications in finance, where regime-switching diffusions are used to introduce stochastic volatility to the asset price model. [ABSTRACT FROM AUTHOR]

To reduce the pollution from the atmosphere or polluted cities like the capital city Delhi of India, use of artificial rain is a solution. In this paper, we have proposed and analyzed a nonlinear mathematical model to reduce the pollution level by rain making. In the proposed model five variables are considered, namely; (i) number density of water vapor, (ii) number density of cloud drops, (iii) number density of raindrops, (iv) cumulative concentration of aerosols, and (v) concentration of pollutant particles suspended in the region of consideration. The effect of environmental fluctuations has been studied with the help of Lyapunov functionals. The model is analyzed in the presence of white noise and proved that if rain persists, the pollutants can be totally washed out. It has been observed that the environmental disturbances are not much favorable in such experiments as the presence of environmental disturbance may destabilize the system. It is found that to remove pollutants completely, it is necessary to prevent the formation of pollutants. The simulation is performed to support the analytical findings. [ABSTRACT FROM AUTHOR]

In this paper, we propose and study a higher order stochastically perturbed SICA epidemic model for HIV transmission. We obtain sufficient conditions for the existence and uniqueness of an ergodic stationary distribution of positive solutions to the system by establishing a series of suitable Lyapunov functions. In a biological point of view, the existence of a stationary distribution indicates that the infectious disease will be prevalent and persistent in the population. Moreover, we make up adequate conditions for complete eradication and wiping out of the infectious disease. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study a time-changed version of the space-time fractional Poisson process (STFPP) by time changing it by an independent Lévy subordinator with finite moments of any order. We call the introduced process as the time-changed fractional Poisson process (TCFPP). The probability mass function, probability generating function and a variant of the law of iterated logarithm for the TCFPP are obtained. Several special cases of the TCFPP are considered and their governing differential equations are derived. Later, we consider the STFPP time-changed by inverse of Lévy subordinator and discuss its particular cases. [ABSTRACT FROM AUTHOR]

Novel coronavirus has altered the socio-economic condition of the whole world through its devastating effects on the human population. Mathematical models and computation techniques may play an important role in understanding this epidemic and contribute a lot in policy making to control the infection in a more systematic and effective way. In this paper, we have proposed a deterministic mathematical model for the Covid-19 pandemic taking into account the different epidemiological status of individuals of a given geographical region and analyzed it with respect to the basic reproduction number. Uncertainty is obvious in the case of a growing epidemic and it multiplies if the disease etiology is unknown. Taking into account the uncertainty in the epidemiological parameters, we extended the deterministic system into a stochastic system through random parameter perturbations in three epidemiological parameters. Analyzing the model, we determined the disease persistence and eradication conditions. The asymptotic behavior of the stochastic solution around the coexistence equilibrium of the deterministic model was also presented. As a case study, we considered the Covid-19 pandemic of India and estimated the model parameters from the epidemic data. We demonstrated different analytical results and predicted the course of the epidemic. Our simulation results indicate that the epidemic in India may continue up to third week of July 2021 and the cumulative confirmed Covid-19 cases may vary from 12.13 × 10 6 to 13.41 × 10 6. Such results may be useful from management and policy development viewpoints. [ABSTRACT FROM AUTHOR]

This paper addresses the global output feedback regulation problem for a class of uncertain feedforward stochastic nonlinear systems. Unlike the previous works, the drift's growth rate depends not only on an unknown constant but also on an output polynomial function and an input function. Moreover, the diffusion's growth rate depends on an unknown constant or an output polynomial function and an input function. By using the general stochastic convergence theorem, an output controller is constructed to guarantee the boundedness and stochastic convergence of the resulting closed-loop system. [ABSTRACT FROM AUTHOR]

In a recent paper by Yu (arXiv:2008.05633, 2020), higher order derivatives of self-intersection local time of fractional Brownian motion were defined, and existence over certain regions of the Hurst parameter H was proved. Utilizing the Wiener chaos expansion, we provide new proofs of Yu's results and show how a Varadhan-type renormalization can be used to extend the range of convergence for the even derivatives. [ABSTRACT FROM AUTHOR]

In this paper, we study the two-person zero-sum stochastic games for controlled continuous time Markov chains with risk-sensitive finite-horizon cost criterion. The transition and cost rates are possibly unbounded. For the zero-sum risk-sensitive stochastic game, we prove the existence of the value of the game and a Markov saddle-point equilibrium in the class of all history-dependent multi-strategies under the suitable conditions. We achieve our results by studying the corresponding risk-sensitive finite-horizon optimality equations. [ABSTRACT FROM AUTHOR]

In this paper we study the discrete approximation of backward stochastic differential equations. Under a kind of non-Lipschitz assumption we prove the convergence of the proposed discrete scheme to the solution of backward stochastic differential equation. [ABSTRACT FROM AUTHOR]

In this paper, a Haar wavelet method is proposed for solving linear and nonlinear stochastic Itô–Volterra integral equation (SIVIE) driven by fractional Brownian motion (FBM) with Hurst parameter H ∈ (1 2 , 1). This approach reduces the solution of the problem under study to the solution of a linear and nonlinear system of algebraic equations, which is based on the operational matrix and stochastic operational matrix of the Haar wavelets basis (HWB). Moreover, the error estimate and convergence analysis of the method are established in detail. Several numerical examples are presented to test the applicability and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The article studies a single-type critical Markov branching process with infinite variance of the offspring distribution. The process admits also an immigration component at the time points of a non-homogeneous Poisson process. If additionally the mean number of immigrants is infinite, then proper limit distributions are obtained, under suitable normalization of the sample paths, depending on the rate of change of the intensity of the Poisson process. [ABSTRACT FROM AUTHOR]

In this work, we first prove the well-posedness of the non-linear martingale problem related to a McKean-Vlasov stochastic differential equation with singular interaction kernel in ℝd for d≥3. The particularity of our setting is that the McKean-Vlasov process we study interacts at each time with all its past time marginal laws by means of a singular space-time kernel. Second, we prove that our stochastic process is a probabilistic interpretation for the parabolic-parabolic Keller-Segel system in ℝd. We thus obtain a well-posedness result to the latter under explicit smallness condition on the parameters of the model. [ABSTRACT FROM AUTHOR]

The distribution of the first-passage time (FPT) Ta for a Brownian particle with drift μ subject to hitting an absorber at a level a > 0 is well-known and given by its density γ (t) = a 2 π t 3 e − (a − μ t) 2 2 t , t > 0 , which is normalized only if μ≥0. This article demonstrates the existence of two additional diffusion process categories (one with one parameter and the other with two) that have the same first passage-time distributions when μ < 0. For both, we identify the transition densities and thoroughly investigate the processes. A substantial implication is that the first-passage time distribution does not indicate whether the process originates from a drifted Brownian motion or from one of the new processes presented. [ABSTRACT FROM AUTHOR]

This article discusses the approximation of probability densities by mixtures of Gaussian densities. The Kullback-Leibler divergence is used as a measure between densities, followed by applications of the EM algorithm. The conditions under which we study these questions are motivated by approximations introduced in non-linear Kalman-type filtering. [ABSTRACT FROM AUTHOR]

In this article, we systematically study the high-order stability for a stochastic reaction-diffusion equation under random fluctuation. We obtain the convergence of solutions and upper semi-continuity of random attractors for the fluctuation equations in the initial space L2(ℝN), without an additional assumption on the nonlinearity. By means of a splitting method, we technically prove that the Lp∩H1-difference of solutions is controlled by the L2-difference of initial values near the initial time, where p , p > 2 , is the dissipation exponent of the nonlinearity. This along with the L2-stability is sufficient for us to establish the Lp∩H1-stability of random attractors. By utilizing some iteration arguments, we show that the solution is uniformly bounded on a bounded interval near the initial time in Lδ(ℝN) for arbitrary δ > p, in which space the convergence of solutions and the upper semi-continuity of random attractors are also established. [ABSTRACT FROM AUTHOR]

This article introduces the notion of global quadratic variation which is based on a notion weaker than the usual tensor topology appearing in the literature of stochastic processes in Banach separable spaces. It is based on convergence in the weak-star topology of approximating sequences. This calculus pursues in the infinite dimensional space the stochastic calculus via regularization introduced by Russo and Vallois for real valued processes and developed in Banach space by Di Girolami and Russo. This article in particular focuses on the relation with classical calculus in order to compare our global quadratic variation with the classical existing notions of quadratic variations in the literature. [ABSTRACT FROM AUTHOR]