In contrast to the classical discrete choice experiment, the respondent in a rank-order discrete choice experiment, is asked to rank a number of alternatives instead of the preferred one. In this paper, we study the information matrix of a rank order nested multinomial logit model (RO.NMNL) and introduce local D-optimality criterion, then we obtain Locally D-optimal design for RO.NMNL models in...

This paper describes an original strategy for using a datadriven probabilistic motion model into particle filter-based target tracking on video streams. Such a model is based on the local motion observed by the camera during a learning phase. Given that the initial, empirical distribution may be incomplete and noisy, we regularize it in a second phase. The hybrid discrete-continuous probabilist...

We consider the analysis of data under mixture models where the number of components in the mixture is unknown. We concentrate on mixture Dirichlet process models, and in particular we consider such models under conjugate priors. This conjugacy enables us to integrate out many of the parameters in the model, and to discretize the posterior distribution. Particle filters are particularly well su...

Finding new families of distributions has become a popular tool in statistical research. In this article, we introduce a new flexible four-parameter discrete model based on the Marshall-Olkin approach, namely, the discrete Kumaraswamy Marshall-Olkin exponential distribution. The proposed distribution can be viewed as another generalization of the geometric distribution and enfolds some importan...

We analytically evaluate the large deviation function in a simple model of classical particle transfer between two reservoirs. We illustrate how the asymptotic long-time regime is reached starting from a special propagating initial condition. We show that the steady-state fluctuation theorem holds provided that the distribution of the particle number decays faster than an exponential, implying ...

Particles and fields are standard components in numerical calculations like transport simulations in nuclear physics and have well-understood dynamics. Still, a common problem is the interaction between particles and fields due to their different formal description. Particle interactions are discrete, pointlike events while field dynamics is described with continuous partial-differential equati...

The early stages of thin film formation are described by a simple hybrid model that couples a set of discrete kinetic rate equations to a Fokker-Planck (FP)-type continuum. Unique features of the atomic processes in energetic particle deposition are outlined and discussed. A thermal atom deposition process is benchmarked with Zinsmeister's analytical theory [~7] to demonstrate the simplicity an...

This study proposes a model based on the combination of Smoothed Particle Hydrodynamics, Coarse Grained Molecular Dynamics and the Discrete Element Method for the simulation of dispersed solid-liquid flows. The model can deal with a large variety of particle types (non-spherical, elastic, breakable, melting, solidifying, swelling), flow conditions (confined, free-surface, microscopic), and scal...

This paper describes a Bayesian approach to model selection and state estimation for sensor-based robot tasks. The approach is illustrated with a hybrid model-state estimation example from force-controlled autonomous compliant motion: simultaneous (discrete) Contact Formation recognition and estimation of (continuous) geometrical parameters. Previous research in this area mostly tries to solve ...

We are concerned with Bayesian identification and prediction of a nonlinear discrete stochastic process. The fact, that a nonlinear process can be approximated by a piecewise linear function advocates the use of adaptive linear models. We propose a linear regression model within a Rao-Blackwellized particle filter. The parameters of the linear model are adaptively estimated using a finite mixtu...