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language + fiction

Dark Matter of the English Language—the unwords

Words are easy, like the wind;
Faithful friends are hard to find.
—William Shakespeare

unanimals

Critters that definitely don't exist but, perhaps, should.

The backal is probably a feisty biter while the cakmiran probably has a quizzical look. And I would completely avoid the fangol—he sounds like trouble.

A great exercise for kids and the comedic-at-heart would be to try to draw some of these. What would a gakrin look like? Or a gorderish?

Below are the alphabetically first 4–10 letter single-word unanimals for each letter. In some cases, no names of a given length were generated for a given letter.

—4—
aytt
bebe
bick
caen
calb
dalh
dlol
fibl
file
galg
gaon
haen
hale
ilpa
jang
kall
laot
laro
mard
mean
naal
neat
orot
oton
pate
peof
qaid
radc
ranl
saol
shal
tial
walr
weil
—5—
acter
alome
boloo
brata
cabal
capir
dacwo
daxol
fimat
fogon
gatey
geass
haore
heisa
ihire
kardo
kouse
lalpy
lante
malbe
morci
nlreg
nriwe
oacda
omita
paric
ponga
radka
ramep
saage
saako
teart
ufuse
wease
weatl
—6—
acukoe
agtalt
backal
banher
caidat
calepe
dearle
dolpin
eyrita
fangol
gafala
gakrin
haamet
hadnel
iykile
jacang
kagcet
kurdot
lalper
largoz
mamket
mander
narnla
oammim
ooceat
palyus
patble
rarman
ravlil
seaise
seikol
tarbaa
tonele
usrenl
valiss
waatir
whagit
—7—
amreron
apunaed
baadber
balsidd
cadtole
calfasf
daldaug
dalfiso
eolgeal
eomrarf
fondard
gallish
gamymly
hankrer
hokloru
itarato
jatfish
jatwoss
kaister
lamushe
leittoy
madarle
malfash
narddco
nhucasf
oncigut
ootfoto
pakline
parcata
qicsoor
raacbor
raipins
sablrod
sabrilr
tenlrit
tonmede
vansoar
vatkifh
waldfil
walslil
—8—
anlonfow
arnbwict
baieslel
barnnkor
caeffuse
cakmiran
disteale
erhadiol
geepbuwl
golshowo
halalale
hocscist
loicpalt
lruzgind
mannforl
marppuse
obberose
oosgerle
pandleie
perphist
raaldope
ragprerd
saelling
saistiet
tolrfish
valcunle
wadmfish
wasshail
—9—
anlfilher
beigartal
cacdockud
cagccride
gardefand
gorderish
ipilfoyor
keosildor
laechinee
lhallaeye
malpandie
maltreuge
okrerblon
pallanmer
penrhapor
shipopish
shorgeone
ugoflifes
waadarall
waamesder
—10—
asdrosquod
cackemorel
canzlitbar
gaotemtirh
gorofoshew
hirkaflarl
honkerfosh
mapobanadl
moalarfesg
nearretlee
qoarrorule
raccistech
sancockese
sealdhicnh
waagelidhe
weendefish
—11—
condlidilin
cotarleweer
galafonllar
geatingtink
rellswobgry
soridioatar
wolfendelad

Here are all some lists with common suffixes

*ish camfish gallish gawlish gohfish gurrish jatfish mipkish polmish wamfish gorderish shipopish slarmish soulfish tolrfish wadmfish weendefish

*ile halile iykile weadnrile cragiile file gile

*ale anmale calilale disteale halalale hale saale

*use bampuse caeffuse marppuse kouse ufuse

*her banher coocher lorsher anlfilher wher

*tar codtar mistar soridioatar wortautar

*ole rorole cadtole wurkole cole

*ise seaise shoise guceyrise

VIEW ALL

news + thoughts

Hola Mundo Cover

Sat 21-09-2019

My cover design for Hola Mundo by Hannah Fry. Published by Blackie Books.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Hola Mundo by Hannah Fry. Cover design is based on my 2013 `\pi` day art. (read)

Curious how the design was created? Read the full details.

Markov Chains

Tue 30-07-2019

You can look back there to explain things,
but the explanation disappears.
You'll never find it there.
Things are not explained by the past.
They're explained by what happens now.
—Alan Watts

A Markov chain is a probabilistic model that is used to model how a system changes over time as a series of transitions between states. Each transition is assigned a probability that defines the chance of the system changing from one state to another.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Markov Chains. (read)

Together with the states, these transitions probabilities define a stochastic model with the Markov property: transition probabilities only depend on the current state—the future is independent of the past if the present is known.

Once the transition probabilities are defined in matrix form, it is easy to predict the distribution of future states of the system. We cover concepts of aperiodicity, irreducibility, limiting and stationary distributions and absorption.

This column is the first part of a series and pairs particularly well with Alan Watts and Blond:ish.

Grewal, J., Krzywinski, M. & Altman, N. (2019) Points of significance: Markov Chains. Nature Methods 16:663–664.

1-bit zoomable gigapixel maps of Moon, Solar System and Sky

Mon 22-07-2019

Places to go and nobody to see.

Exquisitely detailed maps of places on the Moon, comets and asteroids in the Solar System and stars, deep-sky objects and exoplanets in the northern and southern sky. All maps are zoomable.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
3.6 gigapixel map of the near side of the Moon, annotated with 6,733. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixel and 10 gigapixel map of the Solar System on 20 July 2019, annotated with 758k asteroids, 1.3k comets and all planets and satellites. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Northern Celestial Hemisphere, annotated with 44 million stars, 74,000 deep-sky objects and 3,000 exoplanets. (details)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
100 megapixle and 10 gigapixel map of the Southern Celestial Hemisphere, annotated with 69 million stars, 88,000 deep-sky objects and 1000 exoplanets. (details)

Quantile regression

Sat 01-06-2019
Quantile regression robustly estimates the typical and extreme values of a response.

Quantile regression explores the effect of one or more predictors on quantiles of the response. It can answer questions such as "What is the weight of 90% of individuals of a given height?"

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Quantile regression. (read)

Unlike in traditional mean regression methods, no assumptions about the distribution of the response are required, which makes it practical, robust and amenable to skewed distributions.

Quantile regression is also very useful when extremes are interesting or when the response variance varies with the predictors.

Das, K., Krzywinski, M. & Altman, N. (2019) Points of significance: Quantile regression. Nature Methods 16:451–452.

Background reading

Altman, N. & Krzywinski, M. (2015) Points of significance: Simple linear regression. Nature Methods 12:999–1000.

Analyzing outliers: Robust methods to the rescue

Sat 30-03-2019
Robust regression generates more reliable estimates by detecting and downweighting outliers.

Outliers can degrade the fit of linear regression models when the estimation is performed using the ordinary least squares. The impact of outliers can be mitigated with methods that provide robust inference and greater reliability in the presence of anomalous values.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Analyzing outliers: Robust methods to the rescue. (read)

We discuss MM-estimation and show how it can be used to keep your fitting sane and reliable.

Greco, L., Luta, G., Krzywinski, M. & Altman, N. (2019) Points of significance: Analyzing outliers: Robust methods to the rescue. Nature Methods 16:275–276.

Background reading

Altman, N. & Krzywinski, M. (2016) Points of significance: Analyzing outliers: Influential or nuisance. Nature Methods 13:281–282.

Two-level factorial experiments

Fri 22-03-2019
To find which experimental factors have an effect, simultaneously examine the difference between the high and low levels of each.

Two-level factorial experiments, in which all combinations of multiple factor levels are used, efficiently estimate factor effects and detect interactions—desirable statistical qualities that can provide deep insight into a system.

They offer two benefits over the widely used one-factor-at-a-time (OFAT) experiments: efficiency and ability to detect interactions.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Two-level factorial experiments. (read)

Since the number of factor combinations can quickly increase, one approach is to model only some of the factorial effects using empirically-validated assumptions of effect sparsity and effect hierarchy. Effect sparsity tells us that in factorial experiments most of the factorial terms are likely to be unimportant. Effect hierarchy tells us that low-order terms (e.g. main effects) tend to be larger than higher-order terms (e.g. two-factor or three-factor interactions).

Smucker, B., Krzywinski, M. & Altman, N. (2019) Points of significance: Two-level factorial experiments Nature Methods 16:211–212.

Background reading

Krzywinski, M. & Altman, N. (2014) Points of significance: Designing comparative experiments.. Nature Methods 11:597–598.