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CKatCook

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Posts posted by CKatCook

  1. I had this huge reply typed up then I wasn't logged in!! :)

    I decided today to start looking for a good bottle of the stuff and discovered how hard it is to find! And indeed, the bottles are small! I didn't get any because I only ran across ONE brand out of three stores! I am going to keep looking, though. I went with a riesling for tonight, and ventured out into a pino noir (sp?) for later. If this was not so much fun I would think I was turning into a lush :) I never bought so much wine in my life! But I am having a good time.

  2. I have been learning about wines lately and really getting into it, I must say, but I have bit of dumb question. What makes "ice wine" different from regular wine. I know it is sweeter and the grapes are frozen, but when in the process are they frozen?

    Thanks!

  3. I think the advice given here is good, even though I never thought of it that way. Having been blessed with a "cast iron stomach" and being a very adventurous eater, I use to walk in and if it looked good and saw it in people's carts, I bought it. Not the wisest of strategies, but I did come out with some good stuff and learned alot. Reading the ingredient list saved me many of times.

  4. Always worth it. Nothing like a little "cook therapy" to get me through a rough day. Turn on the music, pour some wine, cook up something good. And in that order. Now there are times when school is in session and work is going full blast my crock pot has saved me from eating out, takes some forethought, but there was many a days I would have been stuck with take out without it....

  5. Serious food and body issues going on there. When I worked in the theater one way we could tell the anorexic ballerias from the non anorexic ones was the size of hands are always out of proportion to the rest of their body. Considering the picture in the article, yes...she has problems. Sure there are problems with obesity, but there are other ways to go about it helping the issue.

    I also take very big issue with the notion in the quote that rape victims enjoyed the crime...but this is a food board and I will leave it at that.

    Yep, I would have to cook up the most decadant meal I could muster and sit myself down and enjoy every bite...with slurping!

  6. Ok, so I finally did my first bacon yesterday. I cured it for 7 days then went to go and fire up the grill to smoke it. The problem? the grill would not get hot enough to smoke the chips. I have a uniflame with two sides to the heating element, a heat plate over the flames, the grill grate sits about 6 inches above that. I put the chips that soaked for 30 minutes on one side of the grill and left the other side's heat off. The chips would never smoke! the temperture in the grill itself was too high according to what the recipe said. (Ruhlman, Charcuterie) My question is this, should the chips be on the heat plate not the grill grate? If the tempurture hovered a bit higher, how high is too high? I could never get the tempurture below 250, no matter how hard I tried.

    Thanks!

    ps..I wound up finishing it off in the oven, turned out great anyway. A lot more "richer" than store bought. About two pieces was all I could take.. :wub:

  7. On "statistical models" and 'slumps' in cooking and baseball.  Or is Steven 'losing it'?  Are the women suffering from PMS?  Is something wrong with the cooking, or is everything okay?

    Let's start with 'regression toward the mean':

    Suppose we find 100 excellent chefs, with skills far above the average for chefs, and then look at the skills of, say, their apprentices or children.

    Will the skills of the apprentices or children also be as good as those of the 100 top chefs?  Typically no.  The skills of the apprentices or children will be closer to the average for all chefs than are the skills of the 100 top chefs.

    This phenomenon is called 'regression toward the mean' (where 'mean' is synonymous with 'average').

    Here is one case where the cause of regression toward the mean is obvious:

    Send 1000 people to Las Vegas each with $1000 (to be clear, I'm NOT offering to fund this experiment!) to play slot machines for a day or until they lose their $1000 whichever comes first.  Take the 100 people who did the best.

    Give each of these 100 people $1000 again and repeat -- have them play the slot machines for a day or until they lose the $1000, whichever comes first.

    Now separately for each of the 1000 people, the 100 best of the 1000, and the second effort of the 100, find the average winnings.  Call these averages, respectively, X, Y, and Z. Then typically Z < Y and about half the time even Z < X. So, Z moves from Y down toward X and about half the time is actually less than X. This is and example of 'regression toward the mean', that is, Z, from the second effort of the winners from the first effort, moves toward the mean X of the first efforts of the 1000.

    The cause:  On the first effort, The 100 best of the 1000 were just lucky, and their luck didn't hold on the second effort.

    Or more generally, given an effort with some exceptionally good results, likely some of the cause of those results was just luck so that on the next effort usually the performance will 'regress toward the mean' and be less good.

    So, I conclude that regression toward the mean has nothing to do with the cooking of Steven and the women, 'slumps' in baseball, etc., set this topic aside, and move on to something that can get us some progress.

    For hot and cold streaks in baseball and Steven's cold streak in the kitchen, here is a probabilistic (not really 'statistical') explanation:

    Suppose things are arriving one at a time and we notice when they arrive and count the number of arrivals so far.  We start counting at time 0 with 0 arrivals.  Suppose after time t >= 0, have N(t) arrivals.

    Note:  Yes, for each value of t, the count N(t) is what we observe on one 'trial' of an 'experiment' that hypothetically might have been performed many times.

    We make two assumptions:

    Independent Increments:  For time s >= 0, we assume that the 'increment' in arrivals, N(t + s) - N(t), that is, the number of arrivals in time s starting at time t, is 'independent' of all N(u) for u <= t. The intuitive definition of 'independent' is 'has nothing to do with' or 'knowing N(u) for u <= t' does not 'help' in predicting the increment N(t + s) - N(t).

    Note:  A more precise definition would take us into 'currents of sigma-algebras', and let's not go there here.

    Stationary Increments:  The probability distribution of the increment N(t + s) - N(t) depends only on s and is the same for all t.

    From these 'qualitative' assumptions we can show that there must exist some number r >= 0, that we call the 'arrival rate', so that, for each whole number k = 0, 1, 2, ..., the probability of N(t) = k is just

    P( N(t) = k ) = exp(-rt) (rt)^k / k!

    where exp(-rt) is the constant e ~ 2.71828183 (thank you, Google!) raised to the power (-rt).  Also k! is a 'factorial', that is, the product

    k!  = (1) (2) ...  (k)

    To check, we might recall that for x >= 0

    exp(x) = 1 + x + x^2 / 2!  + x^3 / 3!  + ...

    Then 1 = exp(-rt) exp(rt) so that

    1 = P( N(t) = 0 ) + P( N(t) = 1 ) + P( N(t) = 2 ) + ...

    as we want.  How 'bout that!

    The whole collection of N(t) for t >= 0 is a 'Poisson process' with 'arrival rate' r and

    P( N(t) = k ) = exp(-rt) (rt)^k / k!

    is the 'Poisson' distribution with parameter rt.

    If we let T(k) be the time of arrival k, then it follows that

    P( T(1) <= t ) = 1 - exp(-rt)

    So here we have the cumulative distribution of the time until the first arrival.  This distribution is the 'exponential' distribution with parameter r.

    It turns out, curiously, the distribution of T(1) is also the distribution of the time of the next arrival counting from any time!  That is, the Poisson process 'has no memory'.  That is, if just had an arrival or have been waiting for an hour without an arrival, then the distribution of the time until the next arrival is the same!  Or, even if the arrival rate is one an hour and you have been waiting an hour, can't conclude that the next arrival is 'due real soon, now, y'hear?'.

    The corresponding probability density function of T(1) is

    r exp(-rt)

    It follows that the expectation ('mean', 'average') of T(1) is

    E[T(1)] = 1/r

    Similarly the expectation of T(k), the time of the k-th arrival, is

    E[T(k)] = k/r

    and the expectation of N(t) is

    E[N(t)] = rt

    So, the quantity r does look like an average 'arrival rate':  In time t, the average number of arrivals is just rt.  Or, in time t = 1, the average number of arrivals is just r.

    Our assumptions of stationary and independent increments provide a qualitative 'axiomatic' derivation of the Poisson process.  This derivation is nice because often in practice we can have some confidence in the assumptions of stationary and independent increments just intuitively.  Or, "Look, Ma!  No data!".  Or, just do it all with intuitive hand waving!  That is, the assumptions are all qualitative.

    There are similar qualitative axiomatic derivations with slightly weaker assumptions due to each of S. Watanabe and A. Renyi.

    Exercise:  Check that T(k) <= t exactly when N(t) >= k so that

    P( T(k) <= t ) = P( N(t) >= k ).

    It turns out that the times between arrivals:

    T(1), T(2) - T(1), T(3) - T(2), ...

    are independent and have the same distribution

    1 - exp(-rt)

    So, let's connect 'slumps' in Steven's cooking and baseball:

    We let an 'arrival' be a good dish cooked in the kitchen or a base hit in baseball.

    To 'test' for a 'slump', let's tentatively entertain the assumptions of stationary and independent increments.

    Then the arrival times of good kitchen or baseball results will be just

    T(1), T(2), T(3), ...

    Then looking at these times, curiously, commonly people guess that the arrivals are in 'bunches' or 'clumps' separated with some long empty periods or 'slumps'.

    That is, people can observe such bunches and slumps with just a Poisson process with stationary and independent increments and where the times between arrivals are independent with the same distribution, that is, without any underlying 'cause'.

    So, when we see 'bunches' and 'slumps', we might just be looking at a silly, meaningless Poisson process instead of any real cause.

    So, before we insist on finding a cause, we need better evidence than just intuitive eyeball 'bunches' and 'slumps'.

    For Steven, there is no evidence that anything is wrong!  So, Steven, given the data, we reject the hypothesis that you are 'losing it' and conclude that you are healthy after all!   :smile:

    For the women on this thread, the cooking problems do not have to be caused by the conjectured 'women's problems'!  Sorry, men!  Maybe there actually are 'women's problems', but one week of burned stew, omitted ingredients, spilled milk, etc. is not good evidence!

    Men, I KNOW what sad news this can be!   :smile:

    But, wait, there's more!  Suppose we are running a restaurant serving a city of some hundreds of thousands of people.  For each person, when they go to that restaurant is an arrival process although likely not a Poisson process.  But if the people act independently (not always reasonable) and satisfy a mild additional assumption, then on Saturday night from 7 to 8 PM the arrivals will nicely approximate a Poisson process.  Here we are using the 'renewal theorem' that a sum of independent arrival processes converges to a Poisson process as the number of processes grows.

    Similarly for the number of arrivals at the eG Web site.

    Yes, to make this work, we have to pick a time interval where the arrival rate remains constant or generalize slightly to 'non-stationary' Poisson processes.

    Net, with no data at all, we concluded that Steven and the women are all okay!  How 'bout that!  That has to be the end of the movie!   :smile:

    Postscript:  Never mentioned the Gaussian distribution!

    I wonder what it says about my geek cred that I found that not only interesting but funny.....

    edit to add: Don't worry, it happens to the best of us. I could spin stories for days on the things I have botched up.

  8. My wonderful loving husband wants to buy me a wine storage refrigerator that holds 18 bottles as an anniversery gift. I was wondering what brand everyone has had luck with, and what temperture do you set it at for a variety of wines? I drink alot of whites, dabble in reds now and then, but I am still learning about wines and that may change down the road.

    Thanks!

  9. I thought it would be interesting to make some batches then freeze them and have them on hand to bake at a later time. I was wondering though at what point would someone freeze the batch? Right before they go into the oven?

    I made some a while back but they didn't turn out that great, I cannot wait to try some of these recipes!

  10. gallery_41282_4652_50558.jpg

    That's right gang, I cracked the Davinci Code for making pop-tarts at home!  And you'll see that now you can have much more filling inside!

    Pop-Tart Dough

    13 oz Unsalted Butter at room temp

    1/3 C. & 1 T. (110 g) Milk at room temp

    1 Yolk at room temp

    1 t. (6 g)Sugar

    1 t. (6 g) Salt

    3 1/2 C. (645 g) AP Flour

    In food processor, combine butter, milk, yolk, sugar and salt and pulse until roughly blended.  Add the flour and pulse until it just starts to come together.    Form into a disc and wrap in saran wrap, chill at least 3 hours.

    Brown-Sugar Cinnamon Filling

    1 Egg White at room temp

    3/4 C. Powdered Sugar

    1/4 C. Almond slivers or slices

    1/4 C. Brown sugar

    1 T. Cinnamon

    In food processor, combine all ingredients and blend until well combined.

    Assembly

    Roll the dough into a rough square, dusting with flour, until 1/8" thick.  Cut dough into 4"x6" rectangles.  Pair the rectangles for tops and bottoms.  Brush the edges of your bottom with a whisked egg.  Place a large dollop of filling on inside the egg wash, then spread it evenly.  The amount of filling is up to you, but you can see in the pic below how much I did.  Place the top sheet of dough on top and press firmly but gently onto the bottom sheet.  Chill your Pop-Tarts for 30 minutes.

    Oven to 350F.  Bake for abut 15 minutes or until just starting to brown.  Let sit until room temp.  Frost with a milk and powdered sugar frosting with just a bit of cinnamon added.

    You are my food hero for the day ! :wub: I have been trying to make them myself and could never get a good consistancy with the dough. Thank you for posting this!

  11. You know thanks for asking this, I have often wondered and never had the guts to ask. With that said, I don't make much money at all, most of my disposable income is spent on food and food books, cooking equipment, etc. I don't get to travel much, I am a shameless arm chair traveler. I do however hope to start my own business so I can do these things. But for now I am savoring what I can and learning alot from the wonderful people that post here.

  12. You know, I don't know. I never had the opportunity to sample any of the truely high end stuff. But when I go to a wine shop or liquor store and I start looking at bottles, one of the first thing my eyes seem to go to is the price. Then the label, in less the label is really unique. Now I have paid what is for me good money for a good spanish wine without care. Because I knew I like it. So may be it is less about price and more about preferrance...

  13. She gets on my nerves a little too. I mean if (by some strange chance) everyone started eating local and organic there would be a lot of jobs lost for those that work in industries that grow and supply non organic food. And maybe there is some people that just simply don't mind the fact their food is not organic? It just isn't going to work for everyone, I think.

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