10,341 research outputs found

    An automatic adaptive method to combine summary statistics in approximate Bayesian computation

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    To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms

    The impact of temporal sampling resolution on parameter inference for biological transport models

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    Imaging data has become widely available to study biological systems at various scales, for example the motile behaviour of bacteria or the transport of mRNA, and it has the potential to transform our understanding of key transport mechanisms. Often these imaging studies require us to compare biological species or mutants, and to do this we need to quantitatively characterise their behaviour. Mathematical models offer a quantitative description of a system that enables us to perform this comparison, but to relate these mechanistic mathematical models to imaging data, we need to estimate the parameters of the models. In this work, we study the impact of collecting data at different temporal resolutions on parameter inference for biological transport models by performing exact inference for simple velocity jump process models in a Bayesian framework. This issue is prominent in a host of studies because the majority of imaging technologies place constraints on the frequency with which images can be collected, and the discrete nature of observations can introduce errors into parameter estimates. In this work, we avoid such errors by formulating the velocity jump process model within a hidden states framework. This allows us to obtain estimates of the reorientation rate and noise amplitude for noisy observations of a simple velocity jump process. We demonstrate the sensitivity of these estimates to temporal variations in the sampling resolution and extent of measurement noise. We use our methodology to provide experimental guidelines for researchers aiming to characterise motile behaviour that can be described by a velocity jump process. In particular, we consider how experimental constraints resulting in a trade-off between temporal sampling resolution and observation noise may affect parameter estimates.Comment: Published in PLOS Computational Biolog

    Latitudinal Analysis of Twilight Hydroxyl Airglow

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    Latitudinal characterizations of twilight mesospheric hydroxyl volume emission rate (VER) from year 2002 to 2005, are made possible using the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) sensor, a ten-channel infrared radiometer onboard NASA’s TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics) satellite. Implementation of a binning algorithm over time and geography provides global twilight characteristics from SABER radiometric channel 9 data, centered at ¸ = 1.64 ¹m for the OH (5,3) and OH (4,2) Meinel airglow band infrared emissions, and SABER radiometric channel 8 data, centered at ¸ = 2.06 ¹m for the OH (9,7) and OH (8,6) emissions. The findings show an equatorial effect in both infrared radiometric channels. Faster rise rates are observed at sunset while slower fall rates are observed at sunrise near the equator when compared with rates calculated at midlatitudes. Both hydroxyl channels show the most distinct sunset equatorial effects in the year 2002, and the most distinct sunrise equatorial effects in the year 2005

    An automatic adaptive method to combine summary statistics in approximate Bayesian computation

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    To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the main disadvantages of ABC in practical situations, however, is that parameter inference must generally rely on summary statistics of the data. This is particularly the case for problems involving high-dimensional data, such as biological imaging experiments. However, some summary statistics contain more information about parameters of interest than others, and it is not always clear how to weight their contributions within the ABC framework. We address this problem by developing an automatic, adaptive algorithm that chooses weights for each summary statistic. Our algorithm aims to maximize the distance between the prior and the approximate posterior by automatically adapting the weights within the ABC distance function. Computationally, we use a nearest neighbour estimator of the distance between distributions. We justify the algorithm theoretically based on properties of the nearest neighbour distance estimator. To demonstrate the effectiveness of our algorithm, we apply it to a variety of test problems, including several stochastic models of biochemical reaction networks, and a spatial model of diffusion, and compare our results with existing algorithms

    Equivalence of operations with respect to discriminator clones

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    For each clone C on a set A there is an associated equivalence relation, called C-equivalence, on the set of all operations on A, which relates two operations iff each one is a substitution instance of the other using operations from C. In this paper we prove that if C is a discriminator clone on a finite set, then there are only finitely many C-equivalence classes. Moreover, we show that the smallest discriminator clone is minimal with respect to this finiteness property. For discriminator clones of Boolean functions we explicitly describe the associated equivalence relations.Comment: 17 page

    The Kindergarten Journal, Summer 1910

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    Includes the essay, The Value of Character , by Elizabeth Harrison (page 28), Personal Mention (page 20) and Alumnae Report (page 29) by Edna Dean Baker, and Extension (page 25) by J.N. Crouse, co-principal of the Chicago Kindergarten College. Journal editors: Mrs. Todd Lunsford and Mrs. Florence Capronhttps://digitalcommons.nl.edu/harrison-writings/1029/thumbnail.jp

    An Analysis Of The Effectiveness Of Podcasting As A Supplemental Instructional Tool: A Pilot Study

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    Podcasting is the creation of audio or video files for use on iPods and other MP3 players. It allows the user to view or listen to downloadable files wherever or whenever desired. In higher education, podcasting is experiencing extraordinary growth. While a significant volume of literature exists both lauding and lamenting the incorporation of podcasts into university curricula, the authors were unable to find any empirical studies in either the academic or popular press evaluating any benefits or detriments attributable to educational applications of podcasting.  This paper presents the pilot for an empirical study of the effectiveness of podcasting as a course supplement

    Podcasting In Higher Education: Does It Make A Difference?

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    Podcasting is a growing trend in higher education. Major software companies, such as Apple, have dedicated entire websites to podcasting. These podcasts are available to college students to be used as supplemental material for specific coursework at their particular college or university. Unfortunately, due to the new and progressive nature of the technology, empirical studies of the effectiveness of this pedagogical device are rare. This paper presents an empirical study of the effectiveness of podcasting when incorporated as supplemental course material in a university course
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