In this post we’re going to work with time series data, and write R functions to aggregate hourly and daily time series in monthly time series to catch a glimpse of their underlying patterns. For this analysis we’re going to use public meteorological data recorded by the government of the Argentinian province of San Luis. Data about rainfalls, temperature, humidity and in some cases winds, is published in the REM website (Red de Estaciones Meteorológicas, http://www.clima.edu.ar/). Also, here you can download meteorological data (in .csv format) that has been recorded by weather stations around different places from San Luis.
The visualization shows a Bayesian two-sample t test, for simplicity the variance is assumed to be known. It illustrates both Bayesian estimation via the posterior distribution for the effect, and Bayesian hypothesis testing via Bayes factor. The frequentist p-value is also shown. The null hypothesis, H0 is that the effect δ = 0, and the alternative H1: δ ≠ 0, just like a two-tailed t test. You can use the sliders to vary the observed effect (Cohen's d), sample size (n per group) and the prior on δ.
摘要： 自深度學習（deep learning）技術問世後，許多人都相信這將是帶領我們逐步走入「通用 AI」（general AI）夢想的關鍵，企業領導者也都在演講中談及 AI 時代將會如何來臨，然而事情真的如此順利嗎？電腦視覺與 AI 領域專家 Filip Piekniewski 並不這麼認為，近日在部落格的一篇文章中，Piekniewski 也詳細談及對於現今 AI 發展進度的看法，在他看來，已經有許多跡象都顯示出 AI 產業的「凜冬將至。