969 resultados para Bayesian Learning


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La asignatura troncal “Evaluación Psicológica” de los estudios de Psicología y delestudio de grado “Desarrollo humano en la sociedad de la información” de laUniversidad de Girona consta de 12 créditos según la Ley Orgánica de Universidades.Hasta el año académico 2004-05 el trabajo no presencial del alumno consistía en larealización de una evaluación psicológica que se entregaba por escrito a final de curso yde la cual el estudiante obtenía una calificación y revisión si se solicitaba. En el caminohacia el Espacio Europeo de Educación Superior, esta asignatura consta de 9 créditosque equivalen a un total de 255 horas de trabajo presencial y no presencial delestudiante. En los años académicos 2005-06 y 2006-07 se ha creado una guía de trabajopara la gestión de la actividad no presencial con el objetivo de alcanzar aprendizajes anivel de aplicación y solución de problemas/pensamiento crítico (Bloom, 1975)siguiendo las recomendaciones de la Agencia para la Calidad del Sistema Universitariode Cataluña (2005). La guía incorpora: los objetivos de aprendizaje, los criterios deevaluación, la descripción de las actividades, el cronograma semanal de trabajos paratodo el curso, la especificación de las tutorías programadas para la revisión de losdiversos pasos del proceso de evaluación psicológica y el uso del foro para elconocimiento, análisis y crítica constructiva de las evaluaciones realizadas por loscompañeros

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The objective of this study was to estimate genetic parameters for survival and weight of Nile tilapia (Oreochromis niloticus), farmed in cages and ponds in Brazil, and to predict genetic gain under different scenarios. Survival was recorded as a binary response (dead or alive), during harvest time in the 2008 grow-out period. Genetic parameters were estimated using a Bayesian mixed linear-threshold animal model via Gibbs sampling. The breeding population consisted of 2,912 individual fish, which were analyzed together with the pedigree of 5,394 fish. The heritabilities estimates, with 95% posterior credible intervals, for tagging weight, harvest weight and survival were 0.17 (0.09-0.27), 0.21 (0.12-0.32) and 0.32 (0.22-0.44), respectively. Credible intervals show a 95% probability that the true genetic correlations were in a favourable direction. The selection for weight has a positive impact on survival. Estimated genetic gain was high when selecting for harvest weight (5.07%), and indirect gain for tagging weight (2.17%) and survival (2.03%) were also considerable.

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Recent experiments have established that information can be encoded in the spike times of neurons relative to the phase of a background oscillation in the local field potential—a phenomenon referred to as “phase-of-firing coding” (PoFC). These firing phase preferences could result from combining an oscillation in the input current with a stimulus-dependent static component that would produce the variations in preferred phase, but it remains unclear whether these phases are an epiphenomenon or really affect neuronal interactions—only then could they have a functional role. Here we show that PoFC has a major impact on downstream learning and decoding with the now well established spike timing-dependent plasticity (STDP). To be precise, we demonstrate with simulations how a single neuron equipped with STDP robustly detects a pattern of input currents automatically encoded in the phases of a subset of its afferents, and repeating at random intervals. Remarkably, learning is possible even when only a small fraction of the afferents (~10%) exhibits PoFC. The ability of STDP to detect repeating patterns had been noted before in continuous activity, but it turns out that oscillations greatly facilitate learning. A benchmark with more conventional rate-based codes demonstrates the superiority of oscillations and PoFC for both STDP-based learning and the speed of decoding: the oscillation partially formats the input spike times, so that they mainly depend on the current input currents, and can be efficiently learned by STDP and then recognized in just one oscillation cycle. This suggests a major functional role for oscillatory brain activity that has been widely reported experimentally.

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The ground-penetrating radar (GPR) geophysical method has the potential to provide valuable information on the hydraulic properties of the vadose zone because of its strong sensitivity to soil water content. In particular, recent evidence has suggested that the stochastic inversion of crosshole GPR traveltime data can allow for a significant reduction in uncertainty regarding subsurface van Genuchten-Mualem (VGM) parameters. Much of the previous work on the stochastic estimation of VGM parameters from crosshole GPR data has considered the case of steady-state infiltration conditions, which represent only a small fraction of practically relevant scenarios. We explored in detail the dynamic infiltration case, specifically examining to what extent time-lapse crosshole GPR traveltimes, measured during a forced infiltration experiment at the Arreneas field site in Denmark, could help to quantify VGM parameters and their uncertainties in a layered medium, as well as the corresponding soil hydraulic properties. We used a Bayesian Markov-chain-Monte-Carlo inversion approach. We first explored the advantages and limitations of this approach with regard to a realistic synthetic example before applying it to field measurements. In our analysis, we also considered different degrees of prior information. Our findings indicate that the stochastic inversion of the time-lapse GPR data does indeed allow for a substantial refinement in the inferred posterior VGM parameter distributions compared with the corresponding priors, which in turn significantly improves knowledge of soil hydraulic properties. Overall, the results obtained clearly demonstrate the value of the information contained in time-lapse GPR data for characterizing vadose zone dynamics.

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This questionnaire aims to evaluate your experience of taking part in the project you are carrying out at the university. The questionnaire is anonymous and will not take more than 10 minutes of your time to complete. We would appreciate your honest opinion, in order that the data we gather here can be as useful as possible for improving the project.

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A reinforcement learning (RL) method was used to train a virtual character to move participants to a specified location. The virtual environment depicted an alleyway displayed through a wide field-of-view head-tracked stereo head-mounted display. Based on proxemics theory, we predicted that when the character approached within a personal or intimate distance to the participants, they would be inclined to move backwards out of the way. We carried out a between-groups experiment with 30 female participants, with 10 assigned arbitrarily to each of the following three groups: In the Intimate condition the character could approach within 0.38m and in the Social condition no nearer than 1.2m. In the Random condition the actions of the virtual character were chosen randomly from among the same set as in the RL method, and the virtual character could approach within 0.38m. The experiment continued in each case until the participant either reached the target or 7 minutes had elapsed. The distributions of the times taken to reach the target showed significant differences between the three groups, with 9 out of 10 in the Intimate condition reaching the target significantly faster than the 6 out of 10 who reached the target in the Social condition. Only 1 out of 10 in the Random condition reached the target. The experiment is an example of applied presence theory: we rely on the many findings that people tend to respond realistically in immersive virtual environments, and use this to get people to achieve a task of which they had been unaware. This method opens up the door for many such applications where the virtual environment adapts to the responses of the human participants with the aim of achieving particular goals.

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This questionnaire aims to evaluate your experience of taking part in the project you are carrying out at the university. The questionnaire is anonymous and will not take more than 10 minutes of your time to complete. We would appreciate your honest opinion, in order that the data we gather here can be as useful as possible for improving the project.

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This questionnaire aims to evaluate your experience of taking part in the project you are carrying out at the university. The questionnaire is anonymous and will not take more than 10 minutes of your time to complete. We would appreciate your honest opinion, in order that the data we gather here can be as useful as possible for improving the project.

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This questionnaire aims to evaluate your experience of taking part in the project you are carrying out at the university. The questionnaire is anonymous and will not take more than 10 minutes of your time to complete. We would appreciate your honest opinion, in order that the data we gather here can be as useful as possible for improving the project.