Density of generalized Verhulst process and Bessel process with constant drift

Density of Generalized Verhulst Process and Bessel Process with Constant Drift

Bessel processes with constant negative drift have recently appeared as heavy-traffic limits in queueing theory. We derive a closed-form expression for the spectral representation of the transition density of the Bessel process of orderν> −1 with constant driftμ≠ 0. Whenν> -½ andμ< 0, the first term of the spectral expansion is the steady-state gamma density corresponding to the zero principal eigenvalueλ0= 0, followed by an infinite series of terms corresponding to the higher eigenvaluesλn,n= 1,2,…, as well as an integral over the continuous spectrum aboveμ2/2. When −1 <ν< -½ andμ< 0, there is only one eigenvalueλ0= 0 in addition to the continuous spectrum. As well as applications in queueing, Bessel processes with constant negative drift naturally lead to two new nonaffine analytically tractable specifications for short-term interest rates, credit spreads, and stochastic volatility in finance. The two processes serve as alternatives to the CIR process for modelling mean-reverting positive economic variables and have nonlinear infinitesimal drift and variance. On a historical note, the Sturm–Liouville equation associated with Bessel processes with constant negative drift is closely related to the celebrated Schrödinger equation with Coulomb potential used to describe the hydrogen atom in quantum mechanics. Another connection is with D. G. Kendall's pole-seeking Brownian motion.

We consider the problem of distributed detection over a network of sensors using energy measurements. Assuming that the problem is to detect the presence of stochastic signal with arbitrary temporal correlation, we exactly compute the characteristic function of the energy measurement at each sensing node. As it is shown, when the time-bandwidth product W T is large enough, the exact joint probability density function of the measurements at the sensor sites can be closely approximated by a product of non-trivial density functions that depend of some unknown parameters of the source signal and the sensor network. As those parameters can be easily estimated at each sensor node, we propose a modified generalized likelihood ratio test (GLRT). Through numerical experiments we show that this proposal has performance close to the optimal one which use the exact joint density function and with a significant reduction in the consumption of network resources as bandwidth and transmission power.

Fixed-gain AF PLNC over cascaded Nakagami-m fading channels for vehicular communications

We consider in this paper, a spectrum sharing cognitive radio system with a ratio selection scheme; where one out of N independent-and-identically-distributed transmit antennas is selected such that the ratio of the secondary transmitter (ST) to the secondary receiver (SR) channel gain to the interference from the ST to the primary receiver (PR) channel gain is maximized. Although previous works considered perfect, outdated, or partial channel state information at the transmitter, we stress that using such assumptions may lead to a feedback overhead for updating the SR with the ST-PR interference channel estimation. Considering only statistical knowledge of the ST-PR channel gain, we investigate a ratio selection scheme using a mean value (MV)-based power allocation strategy referred to as MV-based scheme. We first provide the exact statistics in terms of probability density function and cumulative distribution function of the secondary channel gain as well as of the interference channel gain. Furthermore, we derive exact cumulative density function of the received signal-to-noise ratio at the SR where the ST uses a power allocation based on instantaneous perfect channel state information (CSI) referred to as CSI-based scheme. These statistics are then used to derive exact closed form expressions of the outage probability, symbol error rate, and ergodic capacity of the secondary system when the interference channel from the primary transmitter (PT) to the SR is ignored. Furthermore, an asymptotical analysis is also carried out for the MV-based scheme as well as for the CSI-based scheme to derive the generalized diversity gain for each. Subsequently, we address the performance analysis based on exact statistics of the combined signal-to-interference-plus-noise ratio at the SR of the more challenging case; when the PT-SR interference channel is considered. Numerical results in a Rayleigh fading environment manifest that the MV-based scheme outperforms the CSI-based scheme provided that a low interference power constraint is deployed, implying that the MV-based scheme is more suitable for practical systems.

This paper has the goal of defining a class of uncertain structural systems for which it is possible to consider an approach able to give the exact response in terms of the probability density function (PDF). The uncertain structures have been identified in the discretized statically determined ones and the approach has been identified in the coupling of the approximated principal deformation modes method (APDM) and of the probability transformation method (PTM). The first one gives the explicit relationships between the response variables and the uncertainty ones, that are exact when the structures are statically determined. The second method allows to determine the explicit relationship between the PDFs of the response and of the uncertainty variables. The results of some applications have confirmed the goodness of these choices and that the proposed approach gives always exact results for both correlated and uncorrelated uncertainty random variables.

This paper investigates the performance analysis of dual selection (i.e., combining user selection and transmit antenna selection) with maximal ratio combining (MRC) over nonidentical imperfect channel estimation in a multiple-input multiple-output system. This paper derives the exact and approximate cumulative density function and probability density function (PDF) for the dual selection with MRC over nonidentical imperfect channel estimation. Using the distribution, the exact and approximate closed-form expressions of the outage probability are derived with quantification of diversity order. Further, this paper derived the exact ergodic capacity and approximate outage capacity of the the dual selection via Gaussian and Gamma approximations. The numerical results show that the capacity of the dual selection with MRC is enhanced by the number of receive antennas as well as the number of user terminals and sum of channel correlation coefficients, while the order of the receive spatial diversity for outage probability is eliminated by the nonidentical channel estimation error.

We consider the exact path sampling of the squared Bessel process and other continuous-time Markov processes, such as the Cox–Ingersoll–Ross model, constant elasticity of variance diffusion model, and confluent hypergeometric diffusions, which can all be obtained from a squared Bessel process by using a change of variable, time and scale transformation, and change of measure. All these diffusions are broadly used in mathematical finance for modeling asset prices, market indices, and interest rates. We show how the probability distributions of a squared Bessel bridge and a squared Bessel process with or without absorption at zero are reduced to randomized gamma distributions. Moreover, for absorbing stochastic processes, we develop a new bridge sampling technique based on conditioning on the first hitting time at the boundary of the state space. Such an approach allows us to simplify simulation schemes. New methods are illustrated with pricing path-dependent options.

Exact distributions of MLEs of regression coefficients in GMANOVA–MANOVA model

This paper contributes a generic probabilistic method to derive explicit exact probability densities for stochastic volatility models. Our method is based on a novel application of the exponential measure change in [Z. Palmowski & T. Rolski (2002) A technique for exponential change of measure for Markov processes, Bernoulli 8(6), 767–785]. With this generic approach, we first derive explicit probability densities in terms of model parameters for several stochastic volatility models with nonzero correlations, namely the Heston 1993, [Formula: see text], and a special case of the [Formula: see text]-Hypergeometric stochastic volatility models recently proposed by [J. Da Fonseca & C. Martini (2016) The [Formula: see text]-Hypergeometric stochastic volatility model, Stochastic Processes and their Applications 126(5), 1472–1502]. Then, we combine our method with a stochastic time change technique to develop explicit formulae for prices of timer options in the Heston model, the [Formula: see text] model and a special case of the [Formula: see text]-Hypergeometric model.

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

This paper investigates the system performance for a wireless system employing maximal-ratio-combining (MRC) over Nakagami-m fading channel with multiple unequal-power co-channel interferers (CCI). It derives the exact closed-form probability density function (PDF) and moment generating function (MGF) expressions of output signal-to-interference ratio (SIR). Using the PDF and MGF of the output SIR, the system outage probability and average bit-error-rate (BER) for noncoherent and coherent modulation system are obtained. Some numerical examples are given to illustrate the effects of various parameters on the system performance. (4 pages)

We investigate the effects of noise reinforcement on a Bessel process of dimension d∈(0,2), and more specifically on the asymptotic behavior of its additive functionals. This leads us to introduce a local time process and its inverse. We identify the latter as an increasing self-similar (time-homogeneous) Markov process, and from this, several explicit results can be deduced.

A new global isomorphism theorem is obtained that expresses the local\ntimes of transient regular diffusions under $P^{x,y}$, in terms of related\nGaussian processes. This theorem immediately gives an explicit description\nof the local times of diffusions in terms of $0$th order squared Bessel\nprocesses similar to that of Eisenbaum and Ray's classical description\nin terms of certain randomized fourth order squared Bessel processes. The\nproofs given are very simple. They depend on a new version of Kac's lemma\nfor $h$-transformed Markov processes and employ little more than standard\nlinear algebra. The global isomorphism theorem leads to an elementary\nproof of the\nMarkov property of the local times of diffusions and to other recent\nresults about the local times of general strongly symmetric Markov\nprocesses. The new version of Kac's lemma gives simple, short proofs of\nDynkin's isomorphism theorem and an unconditioned isomorphism theorem due\nto Eisenbaum.

For a Brownian motion with a constant drift X and its maximum process M,M-X and 2M-X are diffusion processes by the extensions of Lévy's and Pitman's theorems. We show that cM-X is not a Markov process if c∊R∖{0,1,2}. We also give other elementary proofs of Lévy's and Pitman's theorems.