Category Archives: SVD

A new feature: Technical publications of the week

Posted on 18 February 2017 by

I’m beginning a new style of column, called technical publications of the week. While I can’t promise these will be weekly, I will, from time to time, highlight technical publications I’ve recently read which I consider to be noteworthy. I … Continue reading →

Posted in Anthropocene, big data, climate change, climate disruption, data science, data streams, earthquakes, geophysics, global warming, Hyper Anthropocene, Locality Sensitive Hashing, LSH, MinHash, numerical algorithms, numerical analysis, random projections, seismology, subspace projection methods, SVD, the right to be and act stupid, the tragedy of our present civilization, the value of financial assets | 1 Comment

Six cases of models

Posted on 6 June 2016 by

The previous post included an attempt to explain land surface temperatures as estimated by the BEST project using a dynamic linear model including regressions on both quarterly CO2 concentrations and ocean heat content. The idea was to check the explanatory … Continue reading →

Posted in AMETSOC, anemic data, Anthropocene, astrophysics, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, climate, climate change, climate data, climate disruption, climate models, dlm package, dynamic linear models, dynamical systems, environment, fossil fuels, geophysics, Giovanni Petris, global warming, greenhouse gases, Hyper Anthropocene, information theoretic statistics, maths, maximum likelihood, meteorology, model comparison, numerical software, Patrizia Campagnoli, Rauch-Tung-Striebel, Sonia Petrone, state-space models, stochastic algorithms, stochastic search, SVD, time series | 1 Comment

Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS

Posted on 29 July 2015 by

A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading →

Posted in Bayes, Bayesian, dynamic linear models, dynamical systems, forecasting, Kalman filter, mathematics, maths, multivariate statistics, numerical software, open source scientific software, prediction, R, Rauch-Tung-Striebel, state-space models, statistics, stochastic algorithms, SVD, time series | Leave a comment