I have a new paper on Modifying Gibbs sampling to avoid self transitions. The idea is that an ordinary Gibbs sampling update for a variable will often choose a new value that is the same as the old value. That seems like a waste of time, and would better be avoided. It’s not a new idea. I have long been aware of a method from 1996 due to Jun Liu, which reduces self transitions by replacing Gibbs sampling with Metropolis-Hastings updates using a proposal to change to a different value. This reduces self transitions, though not to the minimum possible, since the proposal may be rejected. Then, a ..read more

I’ve forked the GNU ‘plotutils’ package, written mostly by Rob Maier, which contains the ‘graph’ program. I’ve added new features to ‘graph’ to make it a better tool for producing plots from the Linux/Unix/macOS command line. My main motivation is to use it with my Software for Flexible Bayesian Modeling (FBM), which consists of a set of programs to be run from the command line. One crucial FBM program is ‘net-plt’, which displays information from a log file for an MCMC run of a Bayesian neural network model. Here’s an example use of ‘graph’ to display the average squared error on training and ..read more

I’ve released pqR-2020-07-23, a new version of my variant implementation of R.  You can install it on Linux, Windows, or Mac as described at pqR-project.org. Installation must currently be from source, similarly to source installs of R Core versions of R. This version has preliminary implementations of automatic differentiation and of arithmetic on lists. These are both useful for gradient-based optimization, such as maximum likelihood estimation and neural network training, as well as gradient-based MCMC methods. List arithmetic is helpful when dealing with models that have severa ..read more

In this post, fourth in a series (previous posts: Part 1, Part 2, Part 3), I’ll finally talk about some substantive conclusions of the following paper: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, vol. 368, pp. 860-868, 22 May 2020 (released online 14 April 2020).  The paper is also available here, with supplemental materials here. In my previous post, I talked about how the authors estimate the reproduction numbers (R) over time for the four common cold coronavirus, and how these ..read more

This is the third in a series of posts (previous posts: Part 1, Part 2) in which I look at the following paper: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch, Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period, Science, vol. 368, pp. 860-868, 22 May 2020 (released online 14 April 2020).  The paper is also available here, with supplemental materials here. In this post, I’ll look at how the authors estimate the reproduction numbers (R) over time for the four common cold coronavirus, using the proxies for incidence that I discussed in Part 2. The ..read more

I’ve been looking at the following paper, by researchers at Harvard’s school of public health, which was recently published in Science: Kissler, Tedijanto, Goldstein, Grad, and Lipsitch (2020) Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period (also available here, with supplemental materials here). This is one of the papers referenced in my recent post on seasonality of COVID-19. The paper does several things that seem interesting: It looks at past incidence of “common cold” coronaviruses, estimating the viruses’ reproduction numbers (R) over time, and from th ..read more

Will the incidence of COVID-19 decrease in the summer? There is reason to hope that it will, since in temperate climates influenza and the four coronaviruses that are among the causes of the “common cold” do follow a seasonal pattern, with many fewer cases in the summer. If COVID-19 is affected by season, this would obviously be of importance for policies regarding “lockdown” and provision of health care resources. Furthermore, understanding the reasons for seasonal variation might point towards ways of controlling the spread of COVID-19 (caused by a coronavirus sometimes referred to as S ..read more

We all understand how the total number of cases of COVID-19 and the total number of deaths due to COVID-19 are expected to grow exponentially during the early phase of the pandemic — every infected individual is in contact with others, who are unlikely to themselves be infected, and on average infects more than one of them, leading to the number of cases growing by a fixed percentage every day. We also know that this can’t go on forever — at some point, many of the people in contact with an infected individual have already been infected, so they aren’t a source of new infections. Or alternativ ..read more

Understanding the factors affecting whether someone infected with COVID-19 will become seriously ill is important for treatment of patients, for forecasting and planning, and — with factors that can be changed — for personal decisions aimed at reducing risk. Despite our current focus, influenza also remains a serious disease, so understanding its risk factors is also important. Here, I’ll look at some of the evidence on how body mass — formalized as Body Mass Index (BMI, weight in kilograms divided by squared height in metres) — influences prognosis for respiratory diseases. Information specif ..read more

I’ve released a new version of my Software for Flexible Bayesian Modeling and Markov Chain Sampling (FBM). This is the first public release since 2004, with the first release of the precursor software being in 1995. There was a version mostly completed in 2007 that never got released (due to my not getting around to checking that I’d fixed up everything). The new version has the changes from 2007 plus some more recent updates, including new features used for the tests in this paper. FBM implements several general-purpose Markov chain sampling methods, such as Metropolis updates, Hamiltonian (H ..read more