1,721,016 research outputs found
Asymptotic number of clusters for species sampling sequences with non-diffuse base measure
No full textWe investigate the asymptotic clustering structure of species sampling sequences (ξn)n, for which the base measure has atomic components. We prove a stochastic representation for (ξn)n in terms of a latent exchangeable random partition. Then, we study the asymptotic behaviour of the total number of blocks and of the number of blocks with fixed cardinality in the partition generated by (ξn)n
Evaluating the impact of cache optimizations for stencil operations via two programming paradigms
No full textOn the computation of kantorovich-wasserstein distances between two-dimensional histograms by uncapacitated minimum cost flows
No full textIn this work, we present a method to compute the Kantorovich-Wasserstein distance of order 1 between a pair of two-dimensional histograms. Recent works in computer vision and machine learning have shown the benefits of measuring Wasserstein distances of order 1 between histograms with n bins by solving a classical transportation problem on very large complete bipartite graphs with n nodes and n2 edges. The main contribution of our work is to approximate the original transportation problem by an uncapacitated min cost flow problem on a reduced flow network of size O(n) that exploits the geometric structure of the cost function. More precisely, when the distance among the bin centers is measured with the 1-norm or the ∞-norm, our approach provides an optimal solution. When the distance among bins is measured with the 2-norm, (i) we derive a quantitative estimate on the error between optimal and approximate solution; (ii) given the error, we construct a reduced flow network of size O(n)
A preliminary study on modeling the impact of memory hierarchy on stencil operations for cache based architectures
No full textCentral limit theorem in uniform metrics for generalized Kac equations
No full textThe aim of this paper is to give explicit rates for the speed of convergence to equilibrium of the solution of the generalized Kac equation in two strong metrics: the total variation distance (TV) and the uniform metric between characteristic functions (χ0). A fundamental role in our study is played by the probabilistic representation of the solution of the generalized Kac equation as marginal law of a stochastic process which is a weighted random sum of i.i.d. random variables, where the weights are positive and dependent. Exponential bounds for the total variation distance between the solution and the gaussian stationary state of the Kac equation have been proved by Dolera, Gabetta and Regazzini (2009). In our more general setting the equilibrium states are scale mixtures of stable distributions and hence not necessarily gaussian. Therefore we develop new tools based on ideal metrics that are used in the literature for quantitative central limit theorems for i.i.d. random variables in the domain of attraction of a stable distribution. We obtain first exponential bounds in the so-called ”r-smoothed total variation” and in the weighted χr-metric for a suitable r, then we deduce rates of convergence with respect to the “corresponding” uniform metrics TV and χ0
Mixture of Species Sampling Models
Full text linkWe introduce mixtures of species sampling sequences (mSSS) and discuss how these sequences are related to various types of Bayesian models. As a particular case, we recover species sampling sequences with general (not necessarily diffuse) base measures. These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). Extending previous results, we prove that mSSS are obtained by assigning the values of an exchangeable sequence to the classes of a latent exchangeable random partition. Using this representation, we give an explicit expression of the Exchangeable Partition Probability Function of the partition generated by an mSSS. Some special cases are discussed in detail—in particular, species sampling sequences with general base measures and a mixture of species sampling sequences with Gibbs-type latent partition. Finally, we give explicit expressions of the predictive distributions of an mSSS
Policy towards R&D cooperation and industry evolution
No full textIn this paper we theoretically explore the effects of different types
of financial intervention in supporting R&D cooperation in the context
of a dynamic model of network formation and industry evolution. We
consider two kinds of policies. In the first case, the policy maker subsidies the formation of a R&D agreement when such a link increases social welfare in the period (we call this rule a myopic
social welfare maximizing rule). In the second case, the policy maker pays a fixed subsidy for each link maintained by the firm. We find that, while the myopic social welfare maximizing rule has limited (although positive) effect in terms of industry evolution and social welfare, fixed subsidies have a significant impact, with a long lasting effect on market structure (i.e., concentration decreases). The result of a limited effect of a social welfare maximizing rule is due to the fact that within the period private and social interests in link formation basically coincide. On the contrary, fixed subsidies lead the formation of links that, although unprofitable on a myopic base, allow the self-reinforcing mechanism for which newly created knowledge can be used as input for new agreements
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Beta-Product Dependent Pitman-Yor Processes for Bayesian Inference
No full textMultiple time series data may exhibit clustering over time and the clustering effect may change across different series. This paper is motivated by the Bayesian non–parametric modelling of the dependence between clustering effects in multiple time series analysis. We follow a Dirichlet process mixture approach and define a new class of multivariate dependent Pitman-Yor processes (DPY). The proposed DPY are represented in terms of a vector of stickbreaking processes which determines dependent clustering structures in the time series. We
follow a hierarchical specification of the DPY base measure to accounts for various degrees of information pooling across the series. We discuss some theoretical properties of the DPY and use them to define Bayesian non parametric repeated measurement and vector autoregressive models. We provide efficient Monte Carlo Markov Chain algorithms for posterior computation of the proposed models and illustrate the effectiveness of the method with a simulation study and an application to the United States and the European Union business cycles
- …
