So, over on my del.icio.us account, you'll find a link to download the Roku's firmware image. I thought I should mention that in a venue more people will see. I'm keen to share notes with anyone who's also poking at this. To prove you're serious, please include the significance of 192.168.251.0 in any introductory emails on Roku stuff. Feel free to pass this on to others of a curious bent, as I'm sure there's plenty for people to poke at.

The NED (Netflix Device) is quite a slick little box, and I highly recommend it if you're a Netflix Watch-it-now addict like me. Alfred Hitchcock Presents, The Outer Limits, Little Britain... really, this is a great way to watch classic television. It'd be great if Roku would fulfill their obligations under the GPL, as I've got one of their boxes in my grubby little paws as we speak. Whoops, spoke too soon! They have released source: Roku Netflix Player GPL sources. Thank you, Roku!

In other news, I'm between jobs at the moment. Since I'm no longer there, I can mention that I was at Paglo Labs, which is a cool idea. Someday I'll likely regret leaving the place. Unfortunately, the role wasn't really fitting as time went on (but, I stuck it out through the crunch of the Public Beta launch). There are about four people who seem to be warming to the idea of my employ, but I'm always keen to find more potential positions. There are likely two more going into the top of the hopper in the next day or two, but if you know anyone looking for an information person, let me know (that's stuff like data mining, search, or machine learning). Mo' option, mo' better.

Saturday was the unveiling of the Computer History Museum's Difference Engine. Unfortunately, I missed it because I was half-zonked on the couch all day with something like strep throat. That was a bummer, but it's okay, I had a bit of a religious revival.

Trolling around Google Video for something interesting, I came across Google's Tech Talks again. A total gold mine (and it'd be even better if people knew how to use microphones).

Supporting Scalable Online Statistical Processing was really interesting, about using statistical mechanisms and randomized algorithms to numerically optimize really hard SQL queries. I recommend it as a different way of thinking about working with large datasets.

However, the really significant talk for me was The Next Generation of Neural Networks, by Geoffrey Hinton. In it, he presents a new twist on good ole connectionism. This is one hell of a demo, despite the Chomsky-esque annoying political asides.

"I am interested in what you say and would like to subscribe to your newsletter." It reaffirms my belief in the plausibility of neural networks. There's a long way to go until they're practical, but this demo makes it clear that the promise is still there. I'm back in the fold, I believe again. No pulpit-pounding required, no fire and brimstone. Just the promise of heaven in a clean, intuitive, fundamentally simple model.

The best part, though, is that they publish all the code for this on his website. Unfortunately, it's in Matlab; fortunately, it runs in octave. When I've been up to it (ie: the few hours I was less feverish Sunday), I've started porting it to C++ with GSL, mostly to fully understand the underlying structure.

For now, though, I should get back to sleep. Tomorrow morning: to the doctor's, then hopefully to work. I've run out of watchable movies on Netflix on-demand, and, quite frankly, can't stand the idea of sleeping away another day when there's cool stuff to be done.

This week, I did something tantamout to virtual suicide. I went through my social networking profiles and disabled, deactivated, or deleted most of them. After much consideration, it became clear that social networking sites were bad for my relationships, bad for for my social life, and generally bad for me.

Social networking sites are the high-fructose corn syrup of social interaction. High-fructose corn syrup in food is trouble for two reasons: it doesn't nourish you, nor does it fill you up. Similarly, social networking makes you feel like you're involved in another person's life, without providing the nourishing fulfillment meaningful interaction gives. You get a brief glimmer of fulfillment when you read a snippet of your friend's life, but it wears off quickly, leaving you needing more interaction. This means that you'll visit the site again, which is perfect for the provider: they get another set of ad views.

Don't believe me when I say it's less fulfilling? Let's try a thought experiment. You might get a brief burst of warm fuzzies when someone posts pictures of their newborn baby on facebook, but it's short-lived. Contrast this with someone walking around the office with cameraphone pictures of the same baby. There's a real difference in the quality of these two interactions: the in-person interaction is more affecting than the online one. There are myriad reasons for this, but the important thing is that the in-person, in-depth, in-excitement experience is much richer and more fulfilling than the mediated, short, distant experience of facebook.

Of course, high-fructose corn syrup is okay in moderation, when balanced out with something healthy. Enjoy a Coke when you go see a movie or are stuck in the airport. It's a nice thing, in balance. Similarly, social networking is fine if it's balanced out with more healthy interactions. That comes down to a sort of self-control, which is where I have a hard time, and what finally pushed me over the edge.

I'm a bit of a work-a-holic, having a hard time with work-life balance. I have an intense day job at a startup, where I'm a third of the engineering team. When I get home in the evening, I tinker on personal projects: more software, writing a book or two, and studying new things. I find a lot of satisfaction in these things, and therefore overdo it. In fact, I overdo it so much that my friends won't see me (in person) for weeks at a time. But they'll see my facebook status update every other day or so, "Josh looks forward to taking a day off," or "Josh is finally shipping his pet project!"

Social networking sites make it too easy to "work friends in" around your schedule. They're an enabler for this sort of thing, both in scheduling and perception. If I had to go to dinner to see my friends, I would make sure the few hours were well-spent, and connect with the other people. I might even stop thinking about work for a while. Social networking sites, however, reduce the cost of socializing, which is a great thing for keeping in vague touch with people. Unfortunately, this reduces the perceived value of relationships: there's less social capital invested in these short bursts of activity. This, in turn, makes them less seem meaningful to the participants.

The perception of value is a funny thing. In dating, there's a reason people play hard to get. It's the same reason that food you cooked yourself tastes better. Somewhere in the back of our brains, there's a tiny beancounter, keeping track of how much time, money, and emotion we've put into things. This little accountant isn't always rational or consistent, but generally, the more you put into a thing, the more valuable you find it. By reducing the cost of relationships, social networking sites accidentally trick us into thinking our relationships are less valuable.

Of course, often the relationships are less valuable. It is possible to hold truly deep and meaningful discussions with people online. It's a great medium for this, just as television is a great medium for teaching people. In television, you can show animated graphs, moving diagrams, and demonstrate experiments, all with expository notes (think Mythbusters). What's television actually used for? Fear Factor, Maury Povich, and so on. Similarly, social networking sites don't typically make good use of the medium. They encourage lots of short interactions, which are really great for ad revenue, but are terrible for meaningful connections.

Some sites are better than others for this, and allow you to grow a group of really great friends. This, though, also poses a problem. There's always someone out there to listen and offer advice. Therefore, you never have to think for yourself. Which means you never make your own decisions/mistakes in a vacuum. And, correspondingly, you're never forced into full independence. Collaboration is a great tool for developing new ideas, but it might not be the best thing for one's internal life, as it tends to encourage this sort of promiscuous codependency.

Now that the accounts are closed and the bookmarks are deleted, what do I do next? First, I set up a public Google Calendar. Josh's Google Calendar is a good first approximation of whether or not I'm busy at a given time. It's also a good motivator, reminding myself of the fact that I haven't seen people in X days, and maybe I should get out more.

Next, it's time to really clean up my house, so I feel confident in having people around more often. The war on slightly embarassing dustbunnies is nigh. After that, it's time to start collecting people's phone numbers. Along with this, I need to get better about calling people to hang out more often.

Maybe I should just create a new event on facebook and invite everyone.

Introducing TDIK

(obdisclaimer: I don't speak for my employer here, and this is my idea, not theirs.)

Quick summary: Applying machine learning to uncover bottlenecks, predict system capacity, user growth, hardware purchases, and, generally, everything you need to know when running a service-based business. You give it your monitor data, and it gives you a diagnostic and predictive model of your system.

Time Data Into Knowledge (TDIK) is an idea I had a little over a year ago. Since I've talked with several people about it in that time, it's no longer patentable in the US. And, since I haven't actually done more than a proof of concept, I wanted to make a full public disclosure of the idea, in the hopes that it would inspire someone.

Imagine you have a multi-tier application stack, with fairly complete monitors. So, your frontend server, the message queues, databases, and a few backend processes (bulk and on-line). Additionally, assume you've got complete monitors in this stack: the normal machine telemetry (CPU usage, disk capacity, network utilization, etc), as well as application-specific stuff, like number of users, hits per second, message queue depth, etc.

Traditional monitoring systems give you graphs of all this; you do the analysis. The best you can hope for is a big display of all your graphs together, then eyeball them for correlations. You can shuffle them around to make it easier, but it's still human work. This kind of correlation is great for fires, where you have a sudden large shift in two variables and don't care about the precise magnitude of the relations. It's no good for a more valuable, big-picture task: capacity planning, where the relationships are less pronounced and more complicated.

That's where TDIK comes in. There are ways to find out how correlated two datasets are and then extract models of their relationship. You can expand these out to any number of combinations, though it gets much more computationall expensive. Once you have these models, though, they're invaluable.

You can make a model of your system yourself, using your knowledge of it. Your model will probably be darned good, since you built the thing. I've done these, and not only are they fun, they're handy. But there's always dark corners lurking around. What's the performance interaction of running MySQL and Squid on the same host, for instance? They're both memory-intensive, especially when big requests are getting bandied about. Ideally, I'd have separate hardware for them, but, well, you know how that goes.

TDIK, since it's learning the model from scratch every time, will find out how things interact on your particular system. It discovers correlations that don't seem straightforward, but make sense after the fact. Things like "Webserver load is highly correlated with the number of user profile views in full mode" (you eventually discover that someone accidentally left in the debug code that disables template caching there).

TDIK's models are useful for more than troubleshooting, though. You can also use them for planning. For instance, let's say your website has N concurrent users. Would you like to know how many users the current system can support? The model can tell you how many, and which component will be your bottleneck. Or, perhaps you know that you want to be able to support some number of users. The model could tell you how much you'd need to scale each component in your current system to get there.

What about firefighting? Your model reflects the steady-state performance of the overall system. If you have the last few minutes of monitor data, you can quickly re-correlate and see which components don't fit the model. In fact, you can see exactly how much they don't fit the model, and prioritize the order in which your team checks things out.

But why firefight in the first place? Using that same correlation, you can get alerts when the current state deviates appreciably from the model. One things I've always said about nagios alerts is that "They're only as good as your experience and creativity." If you don't know that a failure mode is waiting, you're not going to have a nagios alert prepared for it. TDIK's model obviates that, since it knows your system intimately. It will notice the increase in CPU time versus page hits even if the raw number of hits is low (say, at midnight), letting you identify and avert the morning meltdown.

"So where do I download or buy this product, or pay for the service?" you ask. Well, it's mostly still vapor. I did a small proof-of-concept, and found that modelling this many variables is noisy. And, honestly, I haven't had time to make this happen. It could probably be a startup, but I'm not certain of it yet. Feel free to drop me an email at josh@joshisanerd.com if you feel otherwise.

(Every so often, I have "clever monkey" moments. These are little creative flashes, insight or non-linear solutions to problems. They make me think I might, in fact, be half as clever as people seem to think I am. Which would make me twice as clever as I usually feel.)

For instance, the other evening, I wanted to wash the pillow slips for my feather pillows. You know those little zipper thingies you put around a feather pillow, to contain the feathers that sneak out? Those things.

This process is akin to cracking open a nuclear containment vessel, but instead of blue light that gives you cancer, you get a cloud of loose little feathers that stick to everything and look silly. And, if you simply wash the cover with feathers in it, they stick to the inside of your washing machine and show up on your clothes for weeks to come.

In the past, my solution to this has been to put on a nice linen shirt, go outside, and flail the feathery pillow slip around. Then, I carry out the pillow and whack it around for a bit. There were little white drifts in my back yard last time, in California, in August. And i still had the damned things in my hair for days.

What I really wanted was a second container for this. I consider doing all that inside a trash bag, or a grocery bag. Maybe I could tie the bag up, all puffy, and then beat it around for a bit. All good ideas, but there's still the problem of opening the bag up. What you need is a filter on the bag as you open it up and the air comes out.

Or, you can just stick the whole assembly in the dryer, open it up, and extract in there. Then, close the dryer, and run air through it for a little while. Et voila! All the feathers are handily sequestered in your standard filter, the pillows are delightfully fluffed, and the slip are wonderfully, well, de-fluffed.

I've recently been learning Haskell. As part of that, I'm implementing Huffman Coding. This is my first real project in the language. It's been overall quite pleasant, and has taught me a lot.

The biggest lesson has been thoroughly meta: compression tasks are a great way to learn a language/environment. For this project, I had to learn how to use modules, do I/O, mangle arrays, and define tree structures. It might not look like too much, but that's actually a huge amount of stuff to shove into a couple weekends of hacking.

Other things I've learned (or relearned), in no particular order:

• You'll get a reasonable answer if you hop on IRC to for help in #haskell.
• "You remember the mental leap from imperative languages to OCaml? I had about the same level of change going from OCaml to Haskell." -- My friend Evan, talking about Haskell (he convinced me to try it, at least).
• The language-of-the-month club is incredibly time-consuming. It took forever to get really basic stuff down in a new language (and I even had the benefit of having already coded some in OCaml).
• A good book makes all the difference. I highly recommend The Craft of Functional Programming. It's a great book, even if you've already been programming in other functional languages.

Overall Impression

This is a really great introduction to both functional programming in general, and the Haskell language in particular. I would like to note, though, that it focuses on the HUGS implementation of Haskell, instead of GHC. That's not a big deal, but it seems that GHC is generally preferred by the community.

It's also readable, which I particularly appreciated. At the time, I was commuting by train, and happily lugged this book around. It was perfect for me, as both an introduction to the Haskell environment and a brush-up on functional languages.

Related Material

• __REF(practical_ocaml, Practical OCaml), an awful introduction to a pretty nice language.

Overall Impression

I just cannot express how awful this book is. It's disjointed, disorganized, and poorly-written. Basically, it's a fractal of awful.

On the positive side, it is really nicely bound.

Related Material

• __REF(craft_of_functional_programming, The Craft of Functional Programming), A much, much better book on functional programming.

Reawakening the blog

If I may quote myself rom the first post here:

So, this is the first post using my new "blog engine," which is just a messy set of of perl scripts, make, and the C pre-processor from GCC. At some point, I'll move over to something a bit more appropriate, but this works wonderfully for now. I think.

I never quite got around to fixing it up and settling it in. Then I stopped caring. Now, however, I feel like I should have a more active presence here. I've got a few little projects going, and feel like a personal outlet could be beneficial.

Therefore, I've dusted off the perl scripts, the macros, and the Makefile, and am bringing blog back.

Overall Impression

In 1974, the three-member Pulitzer Prize jury on fiction supported Gravity's Rainbow for the Pulitzer Prize for Fiction. However, the other eleven members of the board overturned this decision, branding the book "unreadable, turgid, overwritten, and obscene."
-- Wikipedia: [[Gravity's Rainbow]]

I have tried to read this book three times now. I finally committed to it: i paid 15$ retail! A few months in, there's sitll about a third to go, but the story is growing on me. It's a very intricate yet vulgar piece of work, something like lacework made of entrails.

Overall Impression

I really cannot read this book. I've tried, time and time again, and I've made some headway. It's one of those math books in the formal school: unbelievably dense, beginning with a set of abstract theorems, then builds up to the useful stuff. But, until you get past some critical point, all you're doing is serious mathematical navel-gazing. Normally, I'm a big fan of equations for equations' sakes, but I need statistics to apply them.

I basically gave up on this book and picked up __REF(practical_statistics, Practical Statistics) instead. It's much easier going, if less rigorous.

Notes

Quotes

Related

• __REF(practical_statistics, Practical Statistics), which replaced this as my textbook.
• __REF(rea_statistics, REA Problem Solvers Statistics), a handy set of worked problems.

Overall Impression

Odalisque was a nice conclusion for the first volumen of the Baroque Cycle. There are a few "erotic" scenes which are moderately titillating, but not really to my taste. Beyond that, I don't have too much to say without looking over the book again. It continues the Baroque Cycle, and is worth reading if you like Stephenson

Quotes

I have some quotes to put in here, but those will have to wait until I get it back from a friend.

Related Material

• __REF(quicksilver, Quicksilver), the first book in the series.
• __REF(king_of_the_vagabonds, King of the Vagabonds), the second book in the series.
• __REF(the_confusion, The Confusion), the second volume (books 4-6) in the Baroque Cycle.

Overall Impression

It's heavy, the print is hideous, and the paper smells funny. As a book, i hate it, but the content is redeeming. If you're looking for a worked example of a given statistical procedure, you can probably find it in this book. And, if you're lucky, it'll be one of the homework problems in the book you're reading.

Notes

Many of the tests in __REF(practical_statistics, Practical Statistics) that get mentioned in this book include a worked example from Langley. Especially helpful was the Wilcoxon Stratified example, since Langley's explanations are often too verbose to actually read (instead, just follow along with his working of the problem)...

Quotes

Related

• __REF(practical_statistics, Practical Statistics), which is the textbook i bought to accompany this solution guide...
• __REF(introduction_to_statistical_inference, Introduction to Statistical Inference), a much less approachable or applicable textbook on the theoretical underpinnings of statistical inference.

Overall Impression

I'm still working my way through this one, so I'm withholding comments...

Quotes

I have some quotes to put in here, but those will have to wait until I get it back from a friend.

Related Material

• __REF(quicksilver, Quicksilver), the first book in the series.
• __REF(king_of_the_vagabonds, King of the Vagabonds), the second book in the series.
• __REF(odalisque, Odalisque), the third book in the series.

Overall Impression

The collection of papers flows nicely, especially with Jacquette's introductions. I will probably be taking notes on each paper individually soon, but that's currently on the back burner.

Overall Impression

This two-volume set is great reading, with a lot of breadth and a little bit of depth. I'm still finishing up the second volume, slowly, trying to enjoy it as I go along.

Quotes

"Enormous effort was expended on the science of grammar--on the the parts of speech, on gender, on mood, tense, the conjugation of verbse, and similar delights" -- vI p29

"All great scientists are poets; many are mystics." -- vI p65

Voltaire was the foremost figure who seized upon the Lisbon earthquake as an opportunity to attack the climate of optimism. The chief target of his attack was what he called the tout est bien philosophy expressed twenty years earlier in Alexander Pope's "Essay on Man." In the preface to the Poèm sur le déastre de Lisbonn, Voltaire made some devastating points. If, he asked, when Lisbon was destroyed, the philosophers had said to the wretched survivors, "Whatever happens is for the best; the heirs of the dead will benefit financially; the building trade will enjoy a boom; animals will grow fat on meals provided by the corpses trapped in the debris; an earthquake is a necessary effect of a neccessary cause; private misfortune must not be overrated; an individualwho is unlucky is contributing to the general good"--would not such a speech be as cruel as the earthequake was destructive? We must admit, said Voltaire, that there is injustice and evil in the world, that there is inexcusable suffering, that there rae inexplicable calamities. It is stupid and self-deluding to pretend that every misfortune is a benefit in disguise. It is folly o believe that Providence will assure safe-conduct to the virtuous. Man is "weak and helpless, ignorant of his destiny, and exposed to terrible dangers, as all must now see." Optimisim must be replaced by realism; at best, but an "apprehensive hope that Providence will lead us through our dangerous world to a happier state." -- vI p82

"There is no evidence, of course, that a single scientific discovery eer was made by induction as conceived by Bacon. On the contrary it is quite clear that to engage in research without the stimulus and guidance of hypotheses, rules, preconceptions, anticipations, control criteria and the like is a hopeless if not indeed frivolous activity. Abundeant spport for this conclusion may be found throughout the literature of science, and proof of the futility of "pure induction" is not lacking in Bacon's own records of research." -- vI p91

Tartaglia did not take Cardan's betrayal lightly. The feud was long and acrid, marked by many challeneges, appeals to the public conscience, and mutual denunciations. Mathematics was a serious business in those days. -- vI p104

Illustrious men are required to say deathless things on their deathbeds. Laplace is said to have departed after expressing the reasonable opinion, "What we know is very slight; what we don't know is immense." De Morgan, observing that "this looks like a parody on Newton's pebbles," claims on close authority to have learned Laplace's very last words: "Man follows only phantoms." -- vI p138

Whatever Clifford tackled was carried through with a drive that reflected not merely his eagerness for mastery but his joy in living things out to the full. He studied French, German, and Spanish because he thought them necessary for his work; Arabic, Greek, and Sanskrit because they were difficult and, because difficult, a challenege; hieroglyphics because they were a riddle. His justification for learning the Morse code and shorthand was that he was interested in all methods of conveying thought, but this was not the only instance where the little boy in him had to be rationalized. -- vI p201

Galton was ont a mathematician but he was mathematically minded. He had what amounted to an obsession to count and measure (one of his maxims was: "Whenever you can, count"), and he applied this urge to anything and everything. In his laboratory he measured heads, noses, arms, legs, color of eyes and hair, breathing power, "strength of pull and of squeeze," keenness of sight and of hearing, reaction time, height, weight, and so on. He compiled statistics of the weather, of the properties of heiresses, of life span, of the inheritance of physical and mental characters. He counted the number of "fidgets" per miute among persons attending lectures--apparently to derive a coefficient of boredom. Middle-aged persons, he found, are medium fidgets, "children are raretly still, while elderly philosophers will sometimes remain rigid for minutes altogethere." He made a "beauty map" of the British Isles, classifying the girls he passed in the streets of various towns as "attractive, indifferent or repellent." The method he employed was to prick holes in a piece of paper, "torn rudely into a cross with a lon leg," which he concealed within his pocket. London ranked highest; Aberdeen lowest. -- vI p252

Then in 1918 [Russell] was sent to prison for pacifist propaganda. The specific charge against him was that he had written a pamplet accusing the United States Army of "intimidating strikes at home." By th eintervention of Arthur Balfour, prison life was made easier for him and he could read and write as much as he liked provided he did no propaganda. In four and a half months he wrote his famous Introduction to Mathematical Philosophy, a book that has brought to many of us the first heady experience of Russell's thought and style, and begane the work for his Analysis of Mind. The governor of the prison found these writings perplexing but unsubversive. Similarly, a warder found nothin objectionable in Russells reply to a question about his religion. When he said that he was an agnostic, the warder asked how to spell it and remarked with a sigh: "Well, there are many religions, but I suppose they all worship the same God." Russell says "this remark kept me cheerful for about a week." -- vII p46

It is easy to imagine that [Wittgenstein] frightened his students. He was impatient, irritable, insulting. He drove himself, scourged himself to achieve understanding, and was no more sparing of his class. THey respected his passionate honesty and, if not petrified, were spurred to hard mental exertion; but two hours of such severee and tense probing exhausted both teacher and pupils. When it was over, Wittegenstein, full of self-reproach, would eagerly seek relief from his ordeal. Often he would "rush off to a cinema immediately after the class ended," taking a friend with him. He would buy a bun or an execrable English cold pork pie and munch it while he watched the film. He insisted on sitting in the front row so that the screen "would occupy his entire field of vision, and his mind would be turned away from the thoughts of the lecture and his feelings of revulsion." No matter how wretched the picture, he became totally absorbed in it. -- vII p62

White suggests dividing the philosophers of the twentieth century into two groups, hedgehogs and foxes. The image (recently revived by Sir Isaiah Berlin in an essay on Tolstoy) is from a line of the Greek poet Archilochus which says: "The fox knows many things, but the hedgehog knows one big thing." We must not expect too much of this image, but it serves to contrast two main tendenceies. On one side are teh metaphysicists, the builders of monuments, who try to see the world in terms of a central concept which is to organized all their attitudes and beliefs. These are the hedgehogs. In their systems there is a place for everything and everyone; all that the seen and unseen world has been, is, and will be is explained. Their philosophies may not always be a comfort, but they are always complete. There are no loose ends or exceptions; loneliness and starfish, molecules and bills of lading, greed and galaxies, time and art are embraced in a single vision. The other tendency is both more modest and more arrogant. Its followers have a poor opinion of metaphysics They deny that hilosophy has to do with world views, that it has any business pronouncing grandly on religion, politics, morals, art or even science. Their tradition offers little support either to political or religious movements, which is one of the reasons why it has so many detractors. The logical-analysis foxes would like to know many little things--instead of one big thing--but they are content to know even one thing, pvoided they can get to know it very well. One may vary the image of the foxes, and think of the second tendency as surgial. Using the sharpest possible instruments of logic and mathematics, these surgeons of philosophy are intent on excising small muddles that, if untended, may grow into big muddles. The object of attention is not the world or man or marols, but sequences of reasoning, sentences, even single words. Logical analysts are philosophers interested in the causes and cure of philosophy. In time they hope to put themselves out of business. -- vII pp69-70

Related

• __REF(rea_statistics, REA Problem Solvers Statistics), a handy set of worked problems.
• __REF(introduction_to_statistical_inference, Introduction to Statistical Inference), a much less approachable or applicable textbook on the theoretical underpinnings of statistical inference.

Heathen Children Get Presents Too Day

I'm currently in Chapel Hill, staying with Kristina. We've done Heathen Children Get Presents Too Day, which is like Christmas, but for those of us who don't fit into the "Christian" category. (I used to have Atheist Children Get Presents Too Day for just myself, but broadened it this year).

Between my folks and K, it's been a very good year for presents. The real wins were a pocket hole jig and the charcoal Lamy Safari, in Fine. I also got the appropriate convertor, which is already loaded with Noodler's Legal Lapis.

First Post

So, this is the first post using my new "blog engine," which is just a messy set of of perl scripts, make, and the C pre-processor from GCC. At some point, I'll move over to something a bit more appropriate, but this works wonderfully for now. I think.

Overall Impression

This is a really good, if hand-wavey, introduction to the ideas behind statistics. The important thing, though, is that it includes instructions for a number statistical tests and a guide to when these tests are applicable. It is therefore ideal for people who are looking for a gentle-ish introduction to statistical tests.

Notes

Most of my notes from this will be found in the RStats library, which is an implementation of most of the tests in the book (as well as some from Introduction to Statistical Inference.

Quotes

A remarkable example of estimating from a sample occured in the Second World War, when German industrial output was estimated by British and American statisticans from the serial numbers on captured equipment. It was like taking a random sample of marbles from a bag containing consecutively numbered marbles from 1 onwards, and then, with the hlp of a simple formula utilizing the size of the sample and the highest number observed in the sample, an estimate can made of the total number of marbles in the bag. According to checks made after the war, many of these estimates were quite as accurate as those made by the Germans themselves.
-- W.A. Wallis and H. V. Robterts, Statistics - A New Approach, Free Press, 1960.

Related

• __REF(rea_statistics, REA Problem Solvers Statistics), a handy set of worked problems.
• __REF(introduction_to_statistical_inference, Introduction to Statistical Inference), a much less approachable or applicable textbook on the theoretical underpinnings of statistical inference.

Overall Impression

Like Quicksilver, this was good fun. It was slightly more enjoyable This was a wonderfully fun read, which I thoroughly enjoyed. There were a few things that bothered me, though. First and foremost was the way Stephenson put words foreshadowing Einstein into Newton's mouth. It's just bad form, in my humble opinion, and felt like a belabored in-joke among those who would find it "ironic."

Quotes

I have some quotes to put in here, but those will have to wait until I get it back from a friend.

Related Material

• __REF(quicksilver, Quicksilver), the first book in the series.
• __REF(odalisque, Odalisque), the third book in the series.
• __REF(the_confusion, The Confusion), the second volume (books 4-6) in the Baroque Cycle.

Overall Impression

(Quicksilver was the first novel I picked up after a long time without reading any novels, so my impressions might be skewwed.)

This was an overall fun read, which I thoroughly enjoyed once I accepted skimming its. There were a few things that bothered me, though. The only one worth mentioning directly was Newton's constantly foreshadowing Einstein. It's just bad form, in my humble opinion, and felt like a belabored in-joke among those who would find it "ironic."

Quotes

I have some quotes to put in here, but those will have to wait until I get it back from a friend.

Related Material

• __REF(king_of_the_vagabonds, King of the Vagabonds), the second book in the series.
• __REF(odalisque, Odalisque), the third book in the series.
• __REF(the_confusion, The Confusion), the second volume (books 4-6) in the Baroque Cycle.