⭕️ #Hints:
Imagine you are watching a rocket take off nearby and measuring the distance it has traveled once each second. In the first couple of seconds your measurements may be accurate to the nearest centimeter, say. However, 5 minutes later as the rocket recedes into space, the accuracy of your measurements may only be good to 100 m, because of the increased distance, atmospheric distortion and a variety of other factors. The data you collect would exhibit (heteroscedasticity)[https://en.wikipedia.org/wiki/Heteroscedasticity].
In statistics, a collection of random variables is #heteroscedastic (or heteroskedastic;[a] from Ancient Greek hetero “different” and skedasis “dispersion”) if there are sub-populations that have different variabilities from others. Here "variability" could be quantified by the variance or any other measure of statistical dispersion. Thus heteroscedasticity is the absence of #homoscedasticity.
Imagine you are watching a rocket take off nearby and measuring the distance it has traveled once each second. In the first couple of seconds your measurements may be accurate to the nearest centimeter, say. However, 5 minutes later as the rocket recedes into space, the accuracy of your measurements may only be good to 100 m, because of the increased distance, atmospheric distortion and a variety of other factors. The data you collect would exhibit (heteroscedasticity)[https://en.wikipedia.org/wiki/Heteroscedasticity].
In statistics, a collection of random variables is #heteroscedastic (or heteroskedastic;[a] from Ancient Greek hetero “different” and skedasis “dispersion”) if there are sub-populations that have different variabilities from others. Here "variability" could be quantified by the variance or any other measure of statistical dispersion. Thus heteroscedasticity is the absence of #homoscedasticity.
Wikipedia
Homoscedasticity and heteroscedasticity
statistical property in which some subpopulations in a collection of random variables have different variabilities from others
The statistical mechanics of Twitter
Gavin Hall, William Bialek
https://arxiv.org/format/1812.07029
We build models for the distribution of social states in Twitter communities. States can be defined by the participation vs silence of individuals in conversations that surround key words, and we approximate the joint distribution of these binary variables using the maximum entropy principle, finding the least structured models that match the mean probability of individuals tweeting and their pairwise correlations. These models provide very accurate, quantitative descriptions of higher order structure in these social networks. The parameters of these models seem poised close to critical surfaces in the space of possible models, and we observe scaling behavior of the data under coarse-graining. These results suggest that simple models, grounded in statistical physics, may provide a useful point of view on the larger data sets now emerging from complex social systems.
Gavin Hall, William Bialek
https://arxiv.org/format/1812.07029
We build models for the distribution of social states in Twitter communities. States can be defined by the participation vs silence of individuals in conversations that surround key words, and we approximate the joint distribution of these binary variables using the maximum entropy principle, finding the least structured models that match the mean probability of individuals tweeting and their pairwise correlations. These models provide very accurate, quantitative descriptions of higher order structure in these social networks. The parameters of these models seem poised close to critical surfaces in the space of possible models, and we observe scaling behavior of the data under coarse-graining. These results suggest that simple models, grounded in statistical physics, may provide a useful point of view on the larger data sets now emerging from complex social systems.
Eternal fave, ⭐️ step-by-step w/ code:
"Static & dynamic network visualization w/ R" by @Ognyanova
https://t.co/C9H3pB6BOg #rstats #dataviz
"Static & dynamic network visualization w/ R" by @Ognyanova
https://t.co/C9H3pB6BOg #rstats #dataviz
🎞 Emergence and Minimal Models in Condensed Matter Physics and Biology
https://www.perimeterinstitute.ca/videos/emergence-and-minimal-models-condensed-matter-physics-and-biology
Nigel Goldenfeld
Abstract
Our ability to understand the physical world has to a large extent depended on the existence of emergent properties, and the separation of scales that permits effective field theory descriptions to be useful. Exploiting this fact, we can construct minimal models that enable efficient calculation of desired quantities, as long as they are insensitive to microscopic details. This works in many instances in physics, and I give some examples drawn from the kinetics of phase transitions mediated by topological defects. In other fields, such as biology, it is not so clear that these concepts are useful, and I will discuss to what extent emergence and effective theories might be useful.
https://www.perimeterinstitute.ca/videos/emergence-and-minimal-models-condensed-matter-physics-and-biology
Nigel Goldenfeld
Abstract
Our ability to understand the physical world has to a large extent depended on the existence of emergent properties, and the separation of scales that permits effective field theory descriptions to be useful. Exploiting this fact, we can construct minimal models that enable efficient calculation of desired quantities, as long as they are insensitive to microscopic details. This works in many instances in physics, and I give some examples drawn from the kinetics of phase transitions mediated by topological defects. In other fields, such as biology, it is not so clear that these concepts are useful, and I will discuss to what extent emergence and effective theories might be useful.
www.perimeterinstitute.ca
Emergence and Minimal Models in Condensed Matter Physics and Biology | Perimeter Institute
Our ability to understand the physical world has to a large extent depended on the existence of emergent properties, and the separation of scales that permits effective field theory descriptions to be useful. Exploiting this fact, we can construct minimal…
Complex Systems Studies
⭕️ https://www.quantamagazine.org/emergence-how-complex-wholes-emerge-from-simple-parts-20181220/
آپارات - سرویس اشتراک ویدیو
What Is Emergence?
How do extraordinarily complex emergent phenomena — like ants assembling themselves into living bridges, or tiny water and air molecules forming into swirling hurricanes — spontaneously arise from systems of much simpler elements? The answer often depends…
با سلام
نشست یکصدوشصتوچهارم باشگاه فیزیک تهران، ساعت ۱۷ روز دوشنبه 3 دیماه 1397، در سالن آمفی تئاتر دانشکده فیزیک دانشگاه تهران (انتهای خیابان کارگرشمالی، روبهروی کوچه نوزدهم) برگزار خواهد شد.
آقای دکتر غلامرضا جعفری، از دانشکده فیزیک دانشگاه شهید بهشتی در این باشگاه از «دادههای بزرگ، نظریه پیچیدگی و دوران پسامدرن» خواهند گفت. آقای علی فرنودی نیز در این باشگاه پرسش ماه را مطرح و خبر نشست را به آگاهی حاضران خواهند رساند.
مخاطبان این باشگاه، عموم علاقهمندان به فیزیک هستند. برای یادآوری به دوستان خود، با چاپ و نصب پوستر باشگاه در محل کار یا محل تحصیل خود، دیگر علاقهمندان فیزیک را آگاه کنید.
عضویت در #باشگاه_فیزیک و حضور در جلسات آن برای عموم علاقهمندان به علم فیزیک آزاد است.
با احترام
انجمن فیزیک ایران
نشست یکصدوشصتوچهارم باشگاه فیزیک تهران، ساعت ۱۷ روز دوشنبه 3 دیماه 1397، در سالن آمفی تئاتر دانشکده فیزیک دانشگاه تهران (انتهای خیابان کارگرشمالی، روبهروی کوچه نوزدهم) برگزار خواهد شد.
آقای دکتر غلامرضا جعفری، از دانشکده فیزیک دانشگاه شهید بهشتی در این باشگاه از «دادههای بزرگ، نظریه پیچیدگی و دوران پسامدرن» خواهند گفت. آقای علی فرنودی نیز در این باشگاه پرسش ماه را مطرح و خبر نشست را به آگاهی حاضران خواهند رساند.
مخاطبان این باشگاه، عموم علاقهمندان به فیزیک هستند. برای یادآوری به دوستان خود، با چاپ و نصب پوستر باشگاه در محل کار یا محل تحصیل خود، دیگر علاقهمندان فیزیک را آگاه کنید.
عضویت در #باشگاه_فیزیک و حضور در جلسات آن برای عموم علاقهمندان به علم فیزیک آزاد است.
با احترام
انجمن فیزیک ایران
🕸 New Course Alert! 💡 Learn about #networks (like this co-authorship network map of physicians publishing on hepatitis C) from both a computer science & an #economics standpoint! https://t.co/yZfOaX65yK
MIT OpenCourseWare
Networks
This course will highlight common principles that permeate the functioning of networks and how the same issues related to robustness, fragility and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical…
Complex Systems Studies
🕸 New Course Alert! 💡 Learn about #networks (like this co-authorship network map of physicians publishing on hepatitis C) from both a computer science & an #economics standpoint! https://t.co/yZfOaX65yK
The course is aimed at developing a systematic understanding and analysis of networks and processes over networks.
⭕️ Course Description
Networks are ubiquitous in our modern society. The World Wide Web that links us to and enables information flows with the rest of the world is the most visible example. But it is only one of many networks within which we are situated. Our social life is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to everybody else in the United States and in the world. Economic and financial markets also look much more like networks than anonymous marketplaces. Firms interact with the same suppliers and customers and use web-like supply chains. Financial linkages, both among banks and between consumers, companies, and banks, also form a network over which funds flow and risks are shared. Systemic risk in financial markets often results from the counterparty risks created within this financial network. Food chains, interacting biological systems, and the spread and containment of epidemics are some of the other natural and social phenomena that exhibit a marked networked structure.
This course will highlight common principles that permeate the functioning of these networks and how the same issues related to robustness, fragility, and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical systems, random graph models, optimization, and game theory, and cover a wide variety of applications including: learning and informational cascades; economic and financial networks; social influence networks; formation of social groups; communication networks and the Internet; consensus and gossiping; spread and control of epidemics; and control and use of energy networks.
⭕️ Course Description
Networks are ubiquitous in our modern society. The World Wide Web that links us to and enables information flows with the rest of the world is the most visible example. But it is only one of many networks within which we are situated. Our social life is organized around networks of friends and colleagues. These networks determine our information, influence our opinions, and shape our political attitudes. They also link us, often through important but weak ties, to everybody else in the United States and in the world. Economic and financial markets also look much more like networks than anonymous marketplaces. Firms interact with the same suppliers and customers and use web-like supply chains. Financial linkages, both among banks and between consumers, companies, and banks, also form a network over which funds flow and risks are shared. Systemic risk in financial markets often results from the counterparty risks created within this financial network. Food chains, interacting biological systems, and the spread and containment of epidemics are some of the other natural and social phenomena that exhibit a marked networked structure.
This course will highlight common principles that permeate the functioning of these networks and how the same issues related to robustness, fragility, and interlinkages arise in several different types of networks. It will both introduce conceptual tools from dynamical systems, random graph models, optimization, and game theory, and cover a wide variety of applications including: learning and informational cascades; economic and financial networks; social influence networks; formation of social groups; communication networks and the Internet; consensus and gossiping; spread and control of epidemics; and control and use of energy networks.