Top Mathematics discussions
NishMath - #prediction
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Amir Najmi@unofficialgoogledatascience.com
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Data scientists and statisticians are continuously exploring methods to refine data analysis and modeling. A recent blog post from Google details a project focused on quantifying the statistical skills necessary for data scientists within their organization, aiming to clarify job descriptions and address ambiguities in assessing practical data science abilities. The authors, David Mease and Amir Najmi, leveraged their extensive experience conducting over 600 interviews at Google to identify crucial statistical expertise required for the "Data Scientist - Research" role.
Statistical testing remains a cornerstone of data analysis, guiding analysts in transforming raw numbers into actionable insights. One must also keep in mind bias-variance tradeoff and how to choose the right statistical test to ensure the validity of analyses. These tools are critical for both traditional statistical roles and the evolving field of AI/ML, where responsible practices are paramount, as highlighted in discussions about the relevance of statistical controversies to ethical AI/ML development at an AI ethics conference on March 8.
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@phys.org
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A new mathematical model developed by the University of Rovira i Virgili's SeesLab research group, along with researchers from Northeastern University and the University of Pennsylvania, has made it possible to predict human mobility between cities with high precision. The model offers a simpler and more efficient way than current systems and is a valuable tool for understanding how people move in different contexts, which is crucial for transport planning, migration studies, and epidemiology. The research was published in the journal *Nature Communications*.
The model builds on traditional "gravitational models," which estimate mobility based on population size and distance between cities. While these models are simple, they lack accuracy. Modern approaches leverage artificial intelligence and machine learning to incorporate many variables besides origin and destination, such as the density of restaurants and schools, and the socio-demographic characteristics of the population. The COVID-19 pandemic highlighted the importance of predicting mobility for understanding the spread and evolution of viruses.
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@vatsalkumar.medium.com
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Recent articles have focused on the practical applications of random variables in both statistics and machine learning. One key area of interest is the use of continuous random variables, which unlike discrete variables can take on any value within a specified interval. These variables are essential when measuring things like time, height, or weight, where values exist on a continuous spectrum, rather than being limited to distinct, countable values. The concept of the probability density function (PDF) helps us to understand the relative likelihood of a variable taking on a particular value within its range.
Another significant tool being explored is the binomial distribution, which can be applied using programs like Microsoft Excel to predict sales success. This distribution is suited to situations where each trial has only two outcomes – success or failure, like a sales call resulting in a deal or not. Using Excel, one can calculate the probability of various sales outcomes based on factors like the number of calls made and the historical success rate, aiding in setting achievable sales goals and comparing performance over time. Also, the differentiation between binomial and poisson distribution is critical for correct data modelling, with binomial experiments requiring fixed number of trials and two outcomes, unlike poisson. Finally, in the world of random variables, a sequence of them conditionally converging to a constant value has been discussed, highlighting that if the sequence converges, knowing it passes through some point doesn't change the final outcome.
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