Any Full Time Poker Players / Christians?
Well, there's very little to say here except that you're wrong in the context of the conversation. Taken out of context (as you often do), the statement itself is not completely correct. Unsurprisingly, it's related to your ability to understand what models are and how you actually create them.
I imagine that most likely you're thinking of the same types of toy models that you've been talking about on and on as if they are effective at modeling reality perfectly. I am "wrong" in the sense that there do exist stochastic games of absolutely perfect information, at least in theory. Whether that's true in reality... You can assert that a coinflip is 50-50, but coinflips are probably not actually 50-50. Lincoln's head is in fact heavy on a penny, and if you spin the coin instead of flipping it the difference is sizable. Similarly, coin flips are also biased based on how the coin is flipped. The 50-50-ness is something that you assert onto the situation, and that assertion may or may not be objectively true. You have to choose to ignore certain biases that exist in reality in order to create the model, or you claim ignorance (which is simply another way of choosing to exclude certain things from the model).
I imagine that most likely you're thinking of the same types of toy models that you've been talking about on and on as if they are effective at modeling reality perfectly. I am "wrong" in the sense that there do exist stochastic games of absolutely perfect information, at least in theory. Whether that's true in reality... You can assert that a coinflip is 50-50, but coinflips are probably not actually 50-50. Lincoln's head is in fact heavy on a penny, and if you spin the coin instead of flipping it the difference is sizable. Similarly, coin flips are also biased based on how the coin is flipped. The 50-50-ness is something that you assert onto the situation, and that assertion may or may not be objectively true. You have to choose to ignore certain biases that exist in reality in order to create the model, or you claim ignorance (which is simply another way of choosing to exclude certain things from the model).
People are always fooled by appearances and following the crowd (generally held assumptions about knowledge).
You should definitely go back to your early undergraduate years and reread some basic stuff from Bayesian probability.
Nope. The problem is still that the model of the thing is not the thing itself. I know plenty about Bayesian probability and Bayesian inference, much more and to a much deeper degree than you. There's so much more to this than just formulas. I know, for example, that Bayesian models rest upon priors. And that when you introduce priors, you're creating a subjective model (edit: the modeler is making arbitrary decisions about what to include and exclude from the model).
The very fact that you're bragging about "knowing about priors" shows how deep your understandings of Bayesian probability really is.
Have you no brain? Let's do some reading for comprehension:
I've highlighted a different part for you, because you apparently missed it. These words here are meant to indicate a transition to a larger context. That is, contextualizing that information in a broader understanding that extends BEYOND "basic stuff." Specifically, I'm talking about how the application of various methods of MODELING and INFERENCE (including, but not limited to Bayesian methods) is dependent upon priors (ie, a priori knowledge). These are very important issues for the general understanding of "mathematical modeling."
I need to keep repeating that the underlying issue is that the model of the thing is not the thing itself.
Let's talk about "objective data" for a moment. Let's say that we run a coin-flipping process and get the following result:
Heads: 50
Tails: 49
This is objective data. It appears to be a fair coin. If you were to try to build the naive model of this coin, you would say that the probability of the next coin being tails is 49/99.
But in fact, if you looked at the objective data again, you would see the following:
...THTHTHTHTHTH...
And it suddenly becomes clear that the naive model is unable to capture the information that is actually out there. It's not that there's something "wrong with the naive model" (in the sense that you made a basic error), but it's that "the naive model is wrong" (in the sense that it is not sufficiently robust to capture the information that's actually available).
You may object because this is a very simple model, but consider the following:
HTTHTTTHTTTHTHHTTTHHTTTTHHTT
What's the probability of the next coin being heads? You can apply different "objective" models to try to figure out what this is, perhaps trying to do searches for patterns that are two or three terms deep (the previous example had a pattern two terms deep). And if you wanted, I could give you much, much more data, but I will also tell you that you're unlikely to find any such patterns (they might actually exist, I've never searched that deeply in this example).
But it turns out that this list is generated by a completely deterministic process and one that would be conjectured to satisfy any test of randomness that one would attempt to apply.
The underlying issue this time is that using stochastic modeling may be an error, and this is an example of a subjective decision that is made even in the face of objective data. Doing things like supposing that the distribution of the data is a normal curve seem perfectly natural and normal, but they're still a degree to which it's all subjective and requires subjective input (even if it's true that "everyone agrees" that this is the right method to use).
The very fact that... never mind. You simply cannot accept the simple fact that you don't understand enough to get what's going on. The main point is simply lost on you.
PS: The main point is that the model of the thing is not the thing itself.
Now you're going to get yourself out of this with the "he cited me out of context" card? And what's the context here anyway? I said:
to which you respond:
Here, both you and I were talking about Bayesian probability in general, not in the context of a specific domain.
You should definitely go back to your early undergraduate years and reread some basic stuff from Bayesian probability.
Nope. The problem is still that the model of the thing is not the thing itself. I know plenty about Bayesian probability and Bayesian inference, much more and to a much deeper degree than you. There's so much more to this than just formulas. I know, for example, that Bayesian models rest upon priors. And that when you introduce priors, you're creating a subjective model (edit: the modeler is making arbitrary decisions about what to include and exclude from the model).
I need to keep repeating that the underlying issue is that the model of the thing is not the thing itself.
And you're saying that determining the priors always requires some sort of a subjective input. And that is absolutely wrong. You can use Bayesian statistics in many fields where you can determine the priors directly from objective data.
Heads: 50
Tails: 49
This is objective data. It appears to be a fair coin. If you were to try to build the naive model of this coin, you would say that the probability of the next coin being tails is 49/99.
But in fact, if you looked at the objective data again, you would see the following:
...THTHTHTHTHTH...
And it suddenly becomes clear that the naive model is unable to capture the information that is actually out there. It's not that there's something "wrong with the naive model" (in the sense that you made a basic error), but it's that "the naive model is wrong" (in the sense that it is not sufficiently robust to capture the information that's actually available).
You may object because this is a very simple model, but consider the following:
HTTHTTTHTTTHTHHTTTHHTTTTHHTT
What's the probability of the next coin being heads? You can apply different "objective" models to try to figure out what this is, perhaps trying to do searches for patterns that are two or three terms deep (the previous example had a pattern two terms deep). And if you wanted, I could give you much, much more data, but I will also tell you that you're unlikely to find any such patterns (they might actually exist, I've never searched that deeply in this example).
But it turns out that this list is generated by a completely deterministic process and one that would be conjectured to satisfy any test of randomness that one would attempt to apply.
The underlying issue this time is that using stochastic modeling may be an error, and this is an example of a subjective decision that is made even in the face of objective data. Doing things like supposing that the distribution of the data is a normal curve seem perfectly natural and normal, but they're still a degree to which it's all subjective and requires subjective input (even if it's true that "everyone agrees" that this is the right method to use).
The very fact that you're bragging about "knowing about priors" shows how deep your understandings of Bayesian probability really is.
PS: The main point is that the model of the thing is not the thing itself.
... I can't even begin to imagine how bitter you must be to spend all this time trying to convince me that you don't actually have weaknesses at math.
Lol. This is what I call an intellectual disgrace. You really have no shame, do you? It is 100% clear to any rational being that when you say
you are talking about priors in the context of Bayesian probability and Bayesian inference, not the whole context of mathematical modeling, let alone philosophy. You don't have a tiny droplet of intellectual honesty in you. I am literally disgusted by your flip-flopping and hypocrisy.
You are more lost than you could imagine. If you think what you're describing here has anything to do with Bayesian probability, you're dead wrong. And if you're not describing anything related to Bayesian probability, then you must be drunk, because I was talking about objectively determined prior probabilities, not "a priori knowledge" which has nothing to do with Bayes. It is so amusing to watch you make one after another silly mistakes while proudly announcing how deep your understanding of the field is.
Alright, let me try to bring some clarity to your confusion. No matter what the coin is (fair or unfair), the probability of each flip is always the same. Which means that it's impossible to extract a pattern. The only way you can have an actual pattern is if the probability is different for different flips in the series. And in order for this to happen, there must be some process which has memory and is determining each flip. That is only a necessary, but not a sufficient condition for you to be able to extract a pattern, however.
And, for the record, no person of average intelligence would even think about looking at the exact order of coin flips to try to predict what the next flip would be. Having said all that, calculating that probability is relatively easy.
And can you explain to the readers why it is difficult to find a pattern, if one exists, or establish that there is no pattern, if one doesn't exist, if the process underlying each coin flip is deterministic?
Let me give you a small riddle. Do you think this data is all you need in order to apply Bayesian inference?
*Babble-babble*... Specifically, I'm talking about how the application of various methods of MODELING and INFERENCE (including, but not limited to Bayesian methods) is dependent upon priors (ie, a priori knowledge). These are very important issues for the general understanding of "mathematical modeling."
I know plenty about Bayesian probability and Bayesian inference... I know, for example, that Bayesian models rest upon priors.
Let's talk about "objective data" for a moment. Let's say that we run a coin-flipping process and get the following result:
Heads: 50
Tails: 49
This is objective data. It appears to be a fair coin. If you were to try to build the naive model of this coin, you would say that the probability of the next coin being tails is 49/99.
But in fact, if you looked at the objective data again, you would see the following:
...THTHTHTHTHTH...
And it suddenly becomes clear that the naive model is unable to capture the information that is actually out there. It's not that there's something "wrong with the naive model" (in the sense that you made a basic error), but it's that "the naive model is wrong" (in the sense that it is not sufficiently robust to capture the information that's actually available).
Heads: 50
Tails: 49
This is objective data. It appears to be a fair coin. If you were to try to build the naive model of this coin, you would say that the probability of the next coin being tails is 49/99.
But in fact, if you looked at the objective data again, you would see the following:
...THTHTHTHTHTH...
And it suddenly becomes clear that the naive model is unable to capture the information that is actually out there. It's not that there's something "wrong with the naive model" (in the sense that you made a basic error), but it's that "the naive model is wrong" (in the sense that it is not sufficiently robust to capture the information that's actually available).
You may object because this is a very simple model, but consider the following:
HTTHTTTHTTTHTHHTTTHHTTTTHHTT
What's the probability of the next coin being heads?
HTTHTTTHTTTHTHHTTTHHTTTTHHTT
What's the probability of the next coin being heads?
And, for the record, no person of average intelligence would even think about looking at the exact order of coin flips to try to predict what the next flip would be. Having said all that, calculating that probability is relatively easy.
But it turns out that this list is generated by a completely deterministic process and one that would be conjectured to satisfy any test of randomness that one would attempt to apply.
The underlying issue this time is that using stochastic modeling may be an error, and this is an example of a subjective decision that is made even in the face of objective data. Doing things like supposing that the distribution of the data is a normal curve seem perfectly natural and normal, but they're still a degree to which it's all subjective and requires subjective input (even if it's true that "everyone agrees" that this is the right method to use).
Lol. This is what I call an intellectual disgrace. You really have no shame, do you? It is 100% clear to any rational being that when you say
you are talking about priors in the context of Bayesian probability and Bayesian inference, not the whole context of mathematical modeling, let alone philosophy.
I know plenty about Bayesian probability and Bayesian inference... I know, for example, that Bayesian models rest upon priors.
I know plenty about Bayesian probability and Bayesian inference, much more and to a much deeper degree than you. There's so much more to this than just formulas. I know, for example, that Bayesian models rest upon priors. And that when you introduce priors, you're creating a subjective model (edit: the modeler is making arbitrary decisions about what to include and exclude from the model).
Is it really that hard to read in context? Do you really need to excise that sentence from the rest in order to make your point? What does that tell you about what you're saying?
You are more lost than you could imagine. If you think what you're describing here has anything to do with Bayesian probability, you're dead wrong. And if you're not describing anything related to Bayesian probability, then you must be drunk, because I was talking about objectively determined prior probabilities, not "a priori knowledge" which has nothing to do with Bayes.
"A priori information"
http://en.wikipedia.org/wiki/Mathema...ri_information
My miswording.
*Unpause*
Alright, let me try to bring some clarity to your confusion. No matter what the coin is (fair or unfair), the probability of each flip is always the same.
Which means that it's impossible to extract a pattern. The only way you can have an actual pattern is if the probability is different for different flips in the series. And in order for this to happen, there must be some process which has memory and is determining each flip. That is only a necessary, but not a sufficient condition for you to be able to extract a pattern, however.
And, for the record, no person of average intelligence would even think about looking at the exact order of coin flips to try to predict what the next flip would be.
And, for the record, no person of average intelligence would even think about looking at the exact order of coin flips to try to predict what the next flip would be.
And can you explain to the readers why it is difficult to find a pattern, if one exists, or establish that there is no pattern, if one doesn't exist, if the process underlying each coin flip is deterministic?
Let me give you a small riddle. Do you think this data is all you need in order to apply Bayesian inference?
Also, applied to what question? The question of whether the next term in the sequence will be heads or tails? The question of whether the sequence itself is stochastic? Or some undefined question that you haven't actually asked?
Your "small riddle" doesn't seem to be answerable as the question is not sufficiently well-formed. Hmmmm... asking questions that demonstrate that there are not well-formed thoughts in your head... I wonder why that seems familiar.
Okay, I'm going to stop at this point. I am tired of your denial of reality and your constant flip-flops. It's a miracle this thread wasn't locked ages ago anyway.
One last word on this. Hopefully, this is another lesson that you are going to learn (as has happened in past threads about morality, gay rights, etc.) and you'll stop making these strong claims about other posters' "weaknesses" in certain fields. It just makes you looks foolish.
One last word on this. Hopefully, this is another lesson that you are going to learn (as has happened in past threads about morality, gay rights, etc.) and you'll stop making these strong claims about other posters' "weaknesses" in certain fields. It just makes you looks foolish.
Meh. I baited you to go in a certain direction to go far, far afield and you didn't take it (like letting you wander into crazy-land in your IID conversation). Oh well.
But you're right that I'm arguing from a position of annoyance. "Chance does not exist in the long run in poker" is a patently false claim unless you assume an infinite bankroll. Otherwise, chance exists in the long run in poker for +EV players because ROR is non-zero. It's bizarre to me that you would have ever made the initial claim in the first place.
But you're right that I'm arguing from a position of annoyance. "Chance does not exist in the long run in poker" is a patently false claim unless you assume an infinite bankroll. Otherwise, chance exists in the long run in poker for +EV players because ROR is non-zero. It's bizarre to me that you would have ever made the initial claim in the first place.
That's right, risk of ruin exists. And with proper bankroll management it can be reduced to a tiny probability. Risk of ruin also exists in every possible job you could ever imagine.
Wriggle all you want. Wow gold is a commodity. It is bought, and sold, for money. That makes it a commodity. Its value goes up, and down.
Risk of ruin also exists in every possible job you could ever imagine.
But you've demonstrated proficient use of shifting definitions, so it does not surprise at all that you would do it again.
From the economist:
http://www.economist.com/economics-a...#node-21529407
Commodity
A comparatively homogeneous product that can typically be bought in bulk. It usually refers to a raw material - oil, cotton, cocoa, silver - but can also describe a manufactured product used to make other things, for example, microchips used in personal computers. Commodities are often traded on commodity exchanges. On AVERAGE, the PRICE of natural commodities has fallen steadily in REAL TERMS in defiance of some predictions that growing CONSUMPTION of non-renewables such as copper would force prices up. At times the oil price has risen sharply in real terms, most notably during the 1970s, but this was due not to the exhaustion of limited supplies but to rationing by the OPEC CARTEL, or war, or fear of it, particularly in the oil-rich Middle East.
A comparatively homogeneous product that can typically be bought in bulk. It usually refers to a raw material - oil, cotton, cocoa, silver - but can also describe a manufactured product used to make other things, for example, microchips used in personal computers. Commodities are often traded on commodity exchanges. On AVERAGE, the PRICE of natural commodities has fallen steadily in REAL TERMS in defiance of some predictions that growing CONSUMPTION of non-renewables such as copper would force prices up. At times the oil price has risen sharply in real terms, most notably during the 1970s, but this was due not to the exhaustion of limited supplies but to rationing by the OPEC CARTEL, or war, or fear of it, particularly in the oil-rich Middle East.
Investopedia explains 'Commodity'
1. The basic idea is that there is little differentiation between a commodity coming from one producer and the same commodity from another producer - a barrel of oil is basically the same product, regardless of the producer. Compare this to, say, electronics, where the quality and features of a given product will be completely different depending on the producer. Some traditional examples of commodities include grains, gold, beef, oil and natural gas. More recently, the definition has expanded to include financial products such as foreign currencies and indexes. Technological advances have also led to new types of commodities being exchanged in the marketplace: for example, cell phone minutes and bandwidth.
1. The basic idea is that there is little differentiation between a commodity coming from one producer and the same commodity from another producer - a barrel of oil is basically the same product, regardless of the producer. Compare this to, say, electronics, where the quality and features of a given product will be completely different depending on the producer. Some traditional examples of commodities include grains, gold, beef, oil and natural gas. More recently, the definition has expanded to include financial products such as foreign currencies and indexes. Technological advances have also led to new types of commodities being exchanged in the marketplace: for example, cell phone minutes and bandwidth.
Are you latching onto the first line from wikipedia?
http://en.wikipedia.org/wiki/Commodity
In economics, a commodity is the generic term for any marketable item produced to satisfy wants or needs. [1]
^ Karl Marx, "A Contributiion to the Critique of Political Economy" contained in the Collected Works of Karl Marx and Frederick Engels: Volume 29 (International Publishers: New York, 1987) p. 269.
Usually when people consider their "job," especially when compared to "playing poker," they are concerned with their income. The "risk of ruin" in life terms (not making enough money to pay the bills) is not really comparable to the "risk of ruin" of a poker bankroll
(for a majority of players, bankroll is considered to be a separate pot of money from your regular life money, so EV does not include paying monthly expenses).
Lol. Keep wriggling.
TBH, gold doesn't really share the industrial uses that other metal-commodities do and it's historically acted more like a currency than a commodity, so there is some merit to the notion that gold is not a commodity.
Again, it's not as subjective as you think, given that there are objective ways to compute risk of ruin. Meaning, you can determine what the approximate probability of going broke is if you follow a certain bankroll management strategy (say, "never play with less than 40 buy-ins").
Unlike you, people don't waste 74.292% of their life worrying about the "proper definitions" of words and concepts, but rather look at the substance of the issues they are facing.
They care about being able to earn enough money to live the life they want to live (which includes paying the bills, having money for all basic needs, as well as money required for other things which render people's lives happy and meaningful). If something is threatening their ability to earn enough money, be it losing their poker bankroll, facing bankruptcy of the business they're running, or simply being fired if they are an employee, the consequences for them are the same. Nobody's going to start thinking in the way "It looks like the company I work for isn't doing particularly well lately and I think there is a significant chance I get fired. If that happens, I will become unemployed and unable to make any money for a significant period of time, which would affect my life and my loved ones' lives negatively. But hey, at least this isn't called risk of ruin! Win!"
If you're talking about recreational players, yes. If you're talking about poker pros, this is false and shows how little you know about the impact of BF and FT's shut down.
Originally Posted by me
for a majority of players, bankroll is considered to be a separate pot of money from your regular life money, so EV does not include paying monthly expenses
This is true. But in the present market, it's still classified as a commodity.
I have no clue what wriggling you think I've been doing. My line has been pretty straight-forward.
Here's a 22-part introduction to investment in commodities.
http://www.investopedia.com/universi...#axzz1kgBXbvSK
When you find one of those that looks a lot like "cars" or "WoW gold" let me know. Or,
http://www.investopedia.com/articles...#axzz1kgBXbvSK
Here's a link on commodity mutual funds. Find one that invests in something like "cars" or "WoW gold" and I'll concede the point.
Here's a 22-part introduction to investment in commodities.
http://www.investopedia.com/universi...#axzz1kgBXbvSK
When you find one of those that looks a lot like "cars" or "WoW gold" let me know. Or,
http://www.investopedia.com/articles...#axzz1kgBXbvSK
Here's a link on commodity mutual funds. Find one that invests in something like "cars" or "WoW gold" and I'll concede the point.
you said earlier
A decent (and imperfect) measure of whether something has "independent value" is whether whatever it is you're considering can be taken somewhere else ("independent") and used as a means to attain currency ("value"). Casino chips must always go back through the casino that created them. Gold in WoW cannot be used anywhere outside of WoW.
You are wriggling because you use criteria to move the boundaries back to where you want them, but then dont apply that criteria thoroughly.
you said earlier
true, but theres no underlying value to the real economy either. Its a pretend economy too. Its all just concepts, and things that we agree to give value to.
you said earlier
true, but theres no underlying value to the real economy either. Its a pretend economy too. Its all just concepts, and things that we agree to give value to.
Gold in WoW can be exchanged for real money . So in that sense, it can be used outside of WoW.
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