Modeling Adoption and Usage of Competing Products

The emergence and wide-spread use of online social networks has led to a dramatic increase on the availability of social activity data. Importantly, this data can be exploited to investigate, at a microscopic level, some of the problems that have captured the attention of economists, marketers and sociologists for decades, such as, e.g., product adoption, usage and competition. In this paper, we propose a continuous-time probabilistic model, based on temporal point processes, for the adoption and frequency of use of competing products, where the frequency of use of one product can be modulated by those of others. This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which we can easily recognize the effect of social influence, recency and competition. We then develop an inference method to efficiently fit the model parameters by solving a convex program. The problem decouples into a collection of smaller subproblems, thus scaling easily to networks with hundred of thousands of nodes. We validate our model over synthetic and real diffusion data gathered from Twitter, and show that the proposed model does not only provides a good fit to the data and more accurate predictions than alternatives but also provides interpretable model parameters, which allow us to gain insights into some of the factors driving product adoption and frequency of use

La producción de alimentos ecológicos

En el presente documento se aborda el estudio del sector de la alimentación ecológica en el mundo y en España mediante el análisis de datos de naturaleza estadística, analítica y comparativa que permitan comprender la situación actual de dicho sector, así como su origen y evolución a lo largo de los añosUniversidad de Sevilla. Grado en Administración y Dirección de Empresa

A hybrid ss-toa wireless geolocation based on path attenuation : robustness investigation under imperfect path loss exponent

[ESP] Uno de los principales requerimientos en las comunicaciones móviles consiste en la localización del terminal móvil. A pesar del incompleto conocimiento de la radio-propagación causada, por ejemplo, por la estimación del exponente de pérdidas (PLE), y las posibles fluctuaciones de dicho exponente, los sistemas inalámbricos de localización deben determinar la posición del móvil con la mayor precisión posible. En este proyecto, se ha estudiado la localización inalámbrica a través del tiempo de llegada (ToA) desde la estación móvil hasta la estación base. El objetivo de este estudio es extender la aplicación del método híbrido del ancho de señal y el tiempo de llegada (SS-ToA) a señales con diferentes formas de onda, por ejemplo, la segunda derivada del pulso Gaussiano y una señal MSK (”minimum-shift keying”), y la investigación del estimador de máxima verosimilitud (ML) en la estimación del ToA en presencia del conocimiento imperfecto del PLE. Se ha evaluado la estimación del ToA basada en el método SS-ToA bajo el conocimiento imperfecto del PLE utilizando técnicas asintóticas de análisis. Entre los cuatro métodos teóricos de estimación obtenidos, la expansión de la serie de Taylor con el cálculo del valor esperado de la derivada cruzada proporciona los resultados más precisos para el análisis teórico del estimador ML. Además, se obtiene que en la región de umbral, el estimador ML supera al estimador MC para valores pequeños del PLE, como ±° = 0:5, en el caso de interiores, y para valores moderado, por ejemplo ±° = 1, en el caso de exteriores. Sin embargo, en la región asintótica, los estimadores MC y ML bajo el conocimiento perfecto del PLE proporcionan mayor precisión que el estimador ML teniendo en cuenta el error en el PLE [ENG] One of the requirements in wireless communications is the knowledge of the mobile location. Despite the uncertain knowledge of the radio propagation caused by, e.g., the estimation of the path loss exponent (PLE), and the possible fluctuation of the PLE, wireless localization systems have to determine the mobile position as accurately as possible. In this thesis, we consider the wireless geolocation or localization using the radio signals based on their time of arrival (ToA). The objective of this work is to extend the application of the hybrid mix of the signal strength and the time of arrival (SS-ToA) to several signal waveforms, e.g., a second-derivative Gaussian monocycle pulse and a minimum-shift keying signal, and to investigate the performance of the maximum likelihood (ML) estimator in the ToA estimation under the imperfect PLE. We evaluate the ToA estimation performance of the ML estimator based on the SS-ToA method under the imperfect PLE by using asymptotic analysis techniques. It appears that among four derivations, the Taylor expansion with the expectation of the cross-derivative provides the most accurate results for analytically capturing the asymptotic performance of the ML estimator In the threshold region, the ML estimator outperforms the maximum correlation (MC) estimator for the small PLE error, e.g., ±° = 0:5 in the indoor case, and the moderate PLE error, e.g., ±° = 1 in the outdoor case. However, in the asymptotic region the MC and ML estimators under the perfect PLE outperform the ML estimator with the imperfect PLE.Escuela Técnica Superior de Ingeniería de Telecomunicació

Learning and Forecasting Opinion Dynamics in Social Networks

Social media and social networking sites have become a global pinboard for exposition and discussion of news, topics, and ideas, where social media users often update their opinions about a particular topic by learning from the opinions shared by their friends. In this context, can we learn a data-driven model of opinion dynamics that is able to accurately forecast opinions from users? In this paper, we introduce SLANT, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data. We then leverage our framework to derive a set of efficient predictive formulas for opinion forecasting and identify conditions under which opinions converge to a steady state. Experiments on data gathered from Twitter show that our model provides a good fit to the data and our formulas achieve more accurate forecasting than alternatives

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

Automatic Bayesian Density Analysis

Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually not flexible enough to deal with the uncertainty inherent to real-world data: they are often restricted to fixed latent interaction models and homogeneous likelihoods; they are sensitive to missing, corrupt and anomalous data; moreover, their expressiveness generally comes at the price of intractable inference. As a result, supervision from statisticians is usually needed to find the right model for the data. However, since domain experts are not necessarily also experts in statistics, we propose Automatic Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible at large. Specifically, ABDA allows for automatic and efficient missing value estimation, statistical data type and likelihood discovery, anomaly detection and dependency structure mining, on top of providing accurate density estimation. Extensive empirical evidence shows that ABDA is a suitable tool for automatic exploratory analysis of mixed continuous and discrete tabular data.Comment: In proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19

Shaping Social Activity by Incentivizing Users

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives