Ron Garcia

@rg9119.bsky.social

Transmogrifying coffee into LaTeX, but now more grumpy and slow about it. University of British Columbia Computer Science

From Searle, Casella, and McCulloch: "In endeavoring to decide whether a set of effects is fixed or random, the
context of the data, the manner in which they were gathered and the environment
from which they came are the determining factors. In considering these points
the important question is that of inference: are the levels of the factor going to
be considered a random sample from a population of values? “Yes”-then the
effects are to be considered as random effects. “No”- then, presumably,
inferences will be made just about the levels occurring in the data and the effects
are considered as fixed effects. Thus when inferences will be made about a
population of effects from which those in the data are considered to be a random
sample, the effects are considered as random; and when inferences are going
to be confined to the effects in the model, the effects are considered fixed."

In classifying data in terms of factors and their levels the feature of interest
is the extent to which different levels of a factor affect the variable of interest.
We refer to this as the eflect of a level of a factor on that variable.
The effects of a factor are always one or other of the two kinds, as has already
been indicated. First are f i x e d eflects, which are the effects attributable to a
finite set of levels of a factor that occur in the data and which are there because
we are interested in them. In Table 1.1 the effects for the factor sex are fixed
effects, as are those for the factors drug and marital status. Further quality
discussion of fixed effects is in Kempthorne (1975). In a different context the
effect on crop yield of three levels of a factor called fertilizer could correspond
to the three different fertilizer regimes used in an agricultural experiment. They
would be three regimes of particular interest, the effects of which we would want
to quantify from the data to be collected from the experiment.
The second kind of effects are random eflects. These are attributable to a
(usually) infinite set of levels of a factor, of which only a random sample are
deemed to occur in the data. For example, four loaves of bread are taken from
each of six batches of bread baked at three different temperatures. Whereas the
effects due to temperature would be considered fixed effects (presumably we
are interested in the particular temperatures used), the effects due to batches
would be considered random effects because the batches chosen would be
considered a random sample of batches from some hypothetical, infinite
population of batches. Since there is definite interest in the particular baking
temperatures used, the statistical concern is to estimate those temperature effects;
they are fixed effects. No assumption is made that the temperatures are selected
at random from a distribution of temperature values. Since, in contrast, this
kind of assumption has t…

screenshot of the previous post, with "a murder of subtweets" circled in bold

An excerpt from McDonald's "Test Theory" book where he grouses about the term "difficulty".

Some gnarly example due to Lévy, where conditional probability according to Kolmogorov's axioms gives answers that make no one happy.

Added my transition to Full Professor effective today, woohoo!

Text from the wikipedia page for Peter Carl Fabergé:
"His time in Europe had inspired him to make pieces that were more than a sum of their parts. As he later described in his own words: "Expensive things interest me little if the value is merely in so many diamonds or pearls"."

A still from the talk "Theories, Solvers and Static Analysis by Abstract Interpretation" demonstrating a loop that converges to 6 for exact reals and converges to 100 for any finite precision

text: "Mathematicians have never been in full agreement on their science, though it is said to be the science of self-evident verities -- absolute, indisputable and definitive. They have always been in controversy over developing aspects of mathematics, and they have always considered their own age to be in a period of crisis."

text from a recent mastodon post about a textbook on computer systems that continues to treat C (incorrectly) as portable assembly language.

$Excerpt from linked paper: "What is probability? Fisher, as everybody knows, was a frequentist, yet I-who profess to interest in such things-was somewhat taken aback in my rereading to find how vehemently he denies that probability is a limiting frequency in an indefinite sequence of repeated 1actuall trials, which is the position that frequentists ordinarily take 24 (1935b 81; 1958a; 1960 5-7; SI 109-10; see also 1922a 312-3; 1925a 700-1; DOE§§ 3, 6; SI 14, 32-3, 44, 114-6). For Fisher, a probability is the fraction of a set, having no distinguishable subsets, that satisfies a given condition (SI 32-3, 55-7, 109-10; 1958a; 1960 5-6; see also 1922a 312-3; 1925a 700-1; 1935b 78; 1945b 129; 1955 75; 1961a 3, 7). For example, in showing the cards of a well-shuffled deck one at a time, exactly one-quarter of the showings will result in hearts-there can be no question about that. And there are no distinguishable subsets; that means that no particular subset of showings, such as the first thirteen or either of the alternate sets of twenty-six, can be expected to be richer in hearts than others. This is a notion that not everyone will find clear and acceptable, but let us at least allow Fisher to describe it in his own terms:"$