# Category Archives: Kalman filter

## Phase Plane plots of COVID-19 deaths *with uncertainties*

I. Introduction. It’s time to fulfill the promise made in “Phase plane plots of COVID-19 deaths“, a blog post from 2nd May 2020, and produce the same with uncertainty clouds about the functional trajectories(*). To begin, here are some assumptions … Continue reading

## What happens when time sampling density of a series matches its growth

This is the newly updated map of COVID-19 cases in the United States, updated, presumably, because of the new emphasis upon testing: How do we know this is the recent of recent testing? Look at the map of active cases: … Continue reading

## Repaired R code for Markov spatial simulation of hurricane tracks from historical trajectories

(Slight update, 28th June 2020.) I’m currently studying random walk and diffusion processes and their connections with random fields. I’m interested in this because at the core of dynamic linear models, Kalman filters, and state-space methods there is a random … Continue reading

## A model of an electrical grid: A vision

Many people seem to view the electrical grid of the future being much like the present one. I think a lot about networks, because of my job. And I especially think a lot about network topologies, although primarily concerning the … Continue reading

## On Smart Data

One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading

## Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)

(Updated 2016-05-08, to provide reference for plateaus of ML functions in vicinity of MLE.) Simpson’s Paradox is one of those phenomena of data which really give Statistics a substance and a role, beyond the roles it inherits from, say, theoretical … Continue reading

## Gavin Simpson updates his temperature analysis

See the very interesting discussion at his blog, From the bottom of the heap. It would be nice to see some information theoretic measures on these results, though.

## dynamic linear model applied to sea-level-rise anomalies

I spent much of the data working up a function for level+trend dynamic linear modeling based upon the dlm package by Petris, Petrone, and Campagnoli, while trying some calculations and code for regime shift detection. One of the test cases … Continue reading

## Solar array with cloud predicting technology launched in WA

Australia’s first grid-connected solar power project with cloud predicting technology launched at Karratha Airport, WA, in bid to smooth solar supply. Source: Solar array with cloud predicting technology launched in WA

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

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