Compared with algorithms specialized for a single distribution universal (also called automatic or black-box) algorithms for continuous distributions were relatively seldom discussed. But they have important advantages for the user: One algorithm coded and tested only once can do the same or even more than a whole library of standard routines. It is only necessary to have a program available that can evaluate the density of the distribution up to a multiplicative factor. In this paper we show that transformed density rejection is well suited to construct universal algorithms suitable for correlation induction which is important for variance reduction in simulation. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

A rejection algorithm - called transformed density rejection - that uses a new method for constructing simple hat functions for an unimodal, bounded density $f$ is introduced. It is based on the idea to transform $f$ with a suitable transformation $T$ such that $T(f(x))$ is concave. $f$ is then called $T$-concave and tangents of $T(f(x))$ in the mode and in a point on the left and right side are used to construct a hat function with table-mountain shape. It is possible to give conditions for the optimal choice of these points of contact. With $T=-1/\sqrt(x)$ the method can be used to construct a universal algorithm that is applicable to a large class of unimodal distributions including the normal, beta, gamma and t-distribution. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

The automatic Algorithm ARI developed in this paper can generate variates from a large class of unimodal discrete distributions. It is only necessary to know the mode of the distribution and to have a subprogram available that can evaluate the probabilities. In a set up step the algorithm constructs a table mountain shaped hat function. Then rejection inversion, a new variant of the rejection method for discrete distributions that needs only one uniform random number per iteration, is used to sample from the desired distribution. It is shown that the expeceted number of iterations is uniformly bounded for all T-concave discrete distributions. Utilizing a simple squeeze or an auxiliary table of moderate size, which is initialized during generation and not in the set up, Algorithm ARI is fast, at least as fast as the fastest known methods designed for the Poisson, binomial and hypergeometric distributions. The set up time of the algorithm is not affected by the size of the domain of the distribution and is about ten times longer than the generation of one variate. Compared with the very fast and well known alias and indexed search methods the set up of Algorithm ARI is much faster but the generation time is about two times slower. More important than the speed is the fact that Algorithm ARI is the first automatic algorithm that can generate samples from discrete distributions with heavy tails. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

The inversion method for generating non-uniform random variates has some advantages compared to other generation methods, since it monotonically transforms uniform random numbers into non-uniform random variates. Hence it is the method of choice in the simulation literature. However, except for some simple cases where the inverse of the cumulative distribution function is a simple function we need numerical methods. Often inversion by ``brute force" is used, applying either very slow iterative methods or linear interpolation of the CDF and huge tables. But then the user has to accept unnecessarily large errors or excessive memory requirements, that slow down the algorithm. In this paper we demonstrate that with Hermite interpolation of the inverse CDF we can obtain very small error bounds close to machine precision. Using our adaptive interval splitting method this accuracy is reached with moderately sized tables that allow for a fast and simple generation procedure. The algorithms described in this paper have been implemented in ANSI C in a library called UNURAN which is available via anonymous ftp. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

In this paper we will show how recent advances in the combinatorics of lattice paths can be applied to solve interesting and nontrivial problems in the theory of queues. The problems we discuss range from classical ones like M^a/M^b/1 systems to open tandem systems with and without global blocking and to queueing models that are related to random walks in a quarter plane like the Flatto-Hahn model or systems with preemptive priorities. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Three different algorithms for the generation of stationary Gaussian time series with given autocorrelation function are presented in this paper. The algorithms have already been suggested in the literature but are not well known and have never been compared before. Interrelations between the different methods, advantages and disadvantages with respect to speed and memory requirements and the range of autocorrelation functions for which the different methods are stable are discussed. The time-complexity of the algorithms and the comparisons of their implementations show that the method twice using the Fourier transform is by far the most efficient if time series of moderate or large length are generated. A tested C-code of the latter algorithm is included as this method is tricky to implement and very difficult to find in the literature. (We know only one reference, that gives a correct algorithm, but there the description is very short and no proof is included.) (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

A sweep-plane algorithm by Lawrence for convex polytope computation is adapted to generate random tuples on simple polytopes. In our method an affine hyperplane is swept through the given polytope until a random fraction (sampled from a proper univariate distribution) of the volume of the polytope is covered. Then the intersection of the plane with the polytope is a simple polytope with smaller dimension. In the second part we apply this method to construct a black-box algorithm for log-concave and T-concave multivariate distributions by means of transformed density rejection. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

Different universal (also called automatic or black-box) methods have been suggested to sample from univariate log-concave distributions. The description of a universal generator for bivariate distributions has not been published up to now. The new algorithm for bivariate log-concave distributions is based on the method of transformed density rejection. In order to construct a hat function for a rejection algorithm the bivariate density is transformed by the logarithm into a concave function. Then it is possible to construct a dominating function by taking the minimum of several tangent planes which are by exponentiation transformed back into the original scale. The choice of the points of contact is automated using adaptive rejection sampling. This means that a point that is rejected by the rejection algorithm is used as additional point of contact until the maximal number of points of contact is reached. The paper describes the details how this main idea can be used to construct Algorithm ULC2D that can generate random pairs from bivariate log-concave distribution with a computable density. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

We give an algorithm that can be used to sample from any discrete log-concave distribution (e.g. the binomial and hypergeometric distributions). It is based on rejection from a discrete dominating distribution that consists of parts of the geometric distribution. The algorithm is uniformly fast for all discrete log-concave distributions and not much slower than algorithms designed for a single distribution. (author's abstract) / Series: Preprint Series / Department of Applied Statistics and Data Processing

The Snail1 transcriptional repressor is a key factor responsible in triggering epithelial to mesenchymal transition. Although Snail1 is widely expressed in early development, it is limited in adult animals to a subset of mesenchymal cells where it has a largely unknown function. In this project we have demonstrated that Snail1 is required to maintain mesenchymal stem cells (MSCs). This effect is associated to the responsiveness to TGF-[beta]1 which showed a strong Snail1 dependence. Snail1-depletion in conditional knock-out adult animals caused a significant decrease in the number of bone marrow-derived MSCs. In culture, Snail1-deficient MSCs prematurely differentiated to osteoblasts or adipocytes and, in contrast to controls, were resistant to the TGF-[beta]1-induced differentiation block. TGF-[beta]1 was unable to up-regulate most of its targets in Snail1 KO MSCs, an effect that was related, but not limited, to defective PTEN repression and Akt activation. Correspondingly, an analysis of human sarcomas also showed enhanced expression of Snail1 in undifferentiated tumors, which was strongly associated with high expression of TGF-[beta] and poor outcome. These results not only demonstrate a new role for Snail1 in TGF-[beta] response and MSC maintenance but also suggest the involvement of MSCs in sarcoma generation. / El repressor transcripcional Snail1 ha estat descrit principalment com el responsable de la inducció de la transició epiteli mesènquima. Encara que Snail1 s’expressa durant les etapes més primerenques del desenvolupament embrionari, la seva expressió en adults es veu limitada en un conjunt de cèl•lules mesenquimals sense saber-se la seva funció. En aquest projecte hem demostrat que Snail1 es requereix per mantenir el fenotip més indiferenciat de les cèl•lules mare del mesènquima. Aquesta funció la fa en part, per la capacitat de resposta de la citoquina TGF-[beta] la qual mostra una força dependència amb Snail1. Quan s’elimina Snail1 en ratolins adults provoca una clara disminució en el nombre de cèl•lules mare de la medul•la òssia. Aquestes cèl•lules en cultiu presenten una clara diferenciació prematura a osteoblasts i adipòcits. Pel contrari, tractaments amb TGF-[beta]1 aturen la diferenciació. El TGF-[beta]1 es incapaç de incrementar moltes dianes en cèl•lules mare del mesènquima aïllades del ratolí deficient per snail1, aquest efecte en part es degut a la repressió de PTEN i l’activació de AKT. L’anàlisi de sarcomes humans ens ha mostrat una alta expressió de Snail1, el qual també es troba associada amb una alta expressió de TGF-[beta] i baixa supervivència. Aquests resultats no només demostren una nova funció per Snail1 en resposta a TGF-[beta] i el manteniment de les MSC, sinó que també suggereix que Snail1 podria participar en la generació del sarcoma.