Bignose

likeability... adj. easy to like; pleasing http://dictionary.reference.com/browse/likeability Besides the fact that you used the wrong word, I don't really know how your post there answers my question about why you think likelihood and probability are different, and where reward and punishment come in. You're drifting much more into game theory here than probability. They are related topics, but not really applicable to the question at hand. Also, if you are going to cite your 'professional career in science', please cite some published works to back it up. I did.

I don't really know why you'd say something like this. Likelihood and probability are used pretty interchangeably in a lot of the math literature. e.g. the probability distribution I wrote above P({data}|x,I) is often called the 'likelihood function'. No mention of 'reward' or 'punishment' there. See Sivia's Data Analysis A Bayesian Tutorial

Let x = the true percentage of picking the correct horse. x = 0 would mean that the race is picked incorrectly every time, x = 1 would mean that the race is picked correctly every time. x will thus be bounded between 0 and 1. What you are interested in is: what is the probability that x takes a certain value given the data and initial guess. Let me denote the set of data as {data} and denote your initial or a priori distribution by I. In equation form, Bayes' Theorem can be written thusly: [math] P(x|\{data\},I) \propto P(\{data\}|x,I) P(x|I) [/math] There is a denominator term P({data}|I), but is does not involve posterior weighting directly, and really just serves to normalize the distribution on the LHS so that integral over all possibilities is exactly 1.0. As for the a priori distribution, P(x|I), you've got some leeway. I would suggest you start with P(x|I)=1, that is all possibilities are equally likely. In short, you don't trust any info. Now, since you are only interested in correct or incorrect, the distribution of P({data}| x,I) can be represented by the binomial distribution. [math] P(\{data\}|x,I) \propto x^R (1-x)^{N-R} [/math] where N is total number of races and R is the number of those that were predicted correctly. So putting this all together... after 100 races, the probability that the 'true' prediction rate is a certain value will be given by [math] P_{100}(x) = C_{100}x^{25}(1-x)^{75} [/math] (where C is again the normalization). If you plot this, it will be very peaked around x = 0.25, because after 100 races and 25 of them predicted correctly, you've got a fair amount of data that 25% is close to his true rate. But as data keeps coming in -- your next 60 races -- the distribution of his true prediction probability is [math] P_{160}(x) = C_{160}x^{31}(1-x)^{129} [/math] (again C is a normalization factor). This distribution will be even more peaked at a value less than 0.25, 0.19375 to be exact. That is, as more and more data comes in, the confidence in the value after 100 races is being eroded. You can use these formulas to also calculate the relative likelihoods of what the 'true' picking percentage is. For example, if you put in x=0.25 versus x= 0.20, the value at x=0.20 is more than 4 times higher than the value at x=0.25. That is, it is 4 times more likely that the picker is truly a 20% correct picker than a 25% picker. And so on. But note that it isn't like it is impossible for a true 25% picker to have hit a streak where he only gets 10% right. If it is truly random, things like that will happen. If you are a fan of baseball, this is like a true 0.300 hitter having an off week and only going 2 for 26. The guy may still be a 0.300 hitter, but there will just be times when they will be worse, and there will be times when they will go like 13 for 26 in a week. It happens. This is about the simplist Bayesian analysis there is, and is essentially the 'is this a fair coin?' question. I don't know much about horse racing, but I know that there are bets that a deeper that simply did this horse win or not. Win, place, show, etc. I don't really know what that means. Popcorn, this really doesn't address the guy's issue at all. This doesn't help answer the question (paraphrasing here), how likely is it that the guy is really a 25% picker but hit a streak of really bad luck, or how likely is it that the guy was really a 10% picker that got super lucky to get to 25%. For example, using your method really skews the analysis. Let me pose a very similar but different question. A casino runs a roulette wheel with only red and black spaces (no 0 or 00 spaces), an equal number of both, and you can bet on red or black. Watching for 24 hours, you record 487 reds and 513 blacks. 48.7% red, or really close to the 50:50 you'd expect if it was fair. What if you watched for an hour, and you saw 56 reds and 44 blacks, or 56.0% red over that hour. Do you really think that the wheel changed by 7.7% just over an hour? Or was it just that luck saw a little more reds than blacks that hour? That's why one has to be very, very careful doing statistical analysis. Just subtracting one frequency from another is rarely a meaningful answer.

Bravo, +1, and I'm not sure it could have been said better, my friend.

I mean, if I thought I could fly, and I jumped off a building, what if I just missed the ground? Then I would be flying! *Hat tip to Douglas Adams I mean, if I ran around my carpets with just socks and shocked a wondrous battery that held my shocks, then I couldn't have to pay an electricity bill anymore! I mean, if only I could speak the language of cats, I would be the world's greatest cat whisperer and be able to put that smug bastard Jackson Galaxy in his place! Wishing is fun. But it isn't science. You need to have some semblance of evidence to back up statements to even start to sniff science. Just posting random wishes doesn't qualify. Sorry.

Not sure what good hitting it with a comet would do towards terraforming Mars.......

Split, perhaps you should re-read your own posts you shouted (all caps) some very strong statements at one point there. I don't mind you changing your position, but I wouldn't advise trying to revise history.

I think you are thinking of something like the steradian, or solid angle: http://en.wikipedia.org/wiki/Solid_angle

Uh huh. If only there was some way to discriminate between something meaningful, and the ravings of a mad man. What could that be.... what could that be. Oh, I have an idea, how about using the idea or ravings and use them to make predictions and compare those predictions to observations. If you want in any way shape or form to be taken seriously on a science forum, I suggest you start demonstrating how your idea makes good predictions. If someone wants ravings, there are plenty of other places on the internet than a science forum to post them... Look, if there is even a little smidgen of rationality in you, understand that you are not the first to claim you 'comprehend all of existence' and so on. There have been many thousands who came before you. There are even several who have posted to this very forum. If you so fervently believe that you have something correct, demonstrate to us how correct it is by using it to make accurate predictions that agree with experiment. Otherwise, why should anyone pay any attention to your idea, and not one of the others of many thousands that claim the exact same thing? Why should anyone believe you over anyone else? What makes your idea special? In my mind, what would make your idea special would be prediction agreement with measurement. If you don't have that, then I'm sorry, but it isn't special. It is more gobbledygook posted to the Internet along with people claiming homeopathy works and aliens caused both 9/11 and my spilling my morning coffee.

[math]\rho \left( \frac{\partial \mathbf{v}}{\partial t} + \mathbf{v} \cdot \nabla \mathbf{v} \right) = - \nabla p + \nabla \mathbf{T} + \mathbf{b}[/math] rho is the fluid denisity v is the fluid velocity t is time p is the pressure T is the stress tensor and b is the body force This equation is known as the Navier Stokes equation for fluids. It is amazingly accurate at making predictions on how fluid moves. Note that there are many reasons fluids are in motion. The left hand side has acceleration & inertia in it. The right hand side has pressure differential, stress, and body forces in it. The above (typically-)second order differential equation also requires some kind of boundary conditions to solve it. And the boundary can be the sources or sinks of the various terms in the equation, such as accelerations or stresses or pressure differential. There are many texts written on fluid mechanics, which I will no reproduce here, but the wikipedia page is a decent free start. http://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_equations If you want to learn more about it, just ask, and I can recommend texts based on what you tell me your background is.

Quick guys, cheese it! He's on to us! He's figured out that science is about crunching fishy numbers. And he knows you guys are hoarding all the magic solenoids for yourselves. It has nothing to do with just stringing together random sciencey words and getting upset when you aren't immediately lauded as a great mind. no sir-ee. All facetiousness aside, robomont, it is incredibly unclear what you are talking about what you are asking. People have been trying their best to help you, but there is a significant communication breakdown. I am sure that we all could communicate better, but in this thread, the breakdown is largely your not making much sense at all. I think you need to be a lot clearer about what you are talking about, and answer direct questions, like what coil are you talking about, and what exactly do you think a magic solenoid is. Getting upset at us because you aren't communicating well is a strategy that will not get you very far.

It is a nice idea, just not universal. By which I mean it doesn't help people who are color blind, for example.

Ok, then. Demonstrate to us that your idea makes meaningful predictions. If you can do that, everyone's mind will be very open. If you can't do that, then you are just posting a story that you want us all to believe. Science accepts beliefs when you can show that the belief makes correct predictions. Demonstrate how correct your predictions are by actually calculating some and comparing them to measurements we have taken. BTW, this is exactly what the galaxy simulations in the NASA link you cited above completed. The took their idea, derived the equations that came from that idea, used those formulas to program a computer, and compared the results from the program to reality. The predictions that came from that simulation were pretty close to what is viewed in reality -- that's why they published the results. If the prediction was way off, they would have returned to the drawing board and tried again. In fact, I suspect that they had to do that many times. What they didn't do is accuse people of not being open minded and continuing to repeat themselves over and over. So, the point is, can you do the same thing? Can you actually make meaningful predictions from your idea? And how do those predictions compare to what is known?

This is meaningless. Entropy and energy, while related, are two different things. Energy has units of energy (like Joules) and entropy has units of Energy per unit temperature (like Joules/Kelvin). One cannot account for the loss of the other. Same thing with phrase like "If momentum included the gap jump of the energy", momentum and energy are two different, while related, things. If one is analyzing a problem and the momentum isn't balancing, you can't just add more energy to make up for it. There has to be sources or sinks of the correct conserved quantity to be meaningful.

Autocorrect flubbed on me. I meant conservation of energy and momentum. Without virtual particles, there is a great deal of phenomena that do not appear to obey conservation of momentum and energy. Hard to ignore that since conservation principles are among the absolute strongest we know today.

But, as best we can tell, there is Dark Matter all around us. See, e.g., http://phys.org/news/2012-08-plenty-dark-sun.html The MM experiment or one of the following experiments should have picked it up. The wikipedia article above shows that in 2009 a very accurate version of an MM-like experiment showed at most a chance of 10^-17 difference in the speed of light due to some form of aether. Also, this explanation of Dark Matter doesn't explain why DM seems to be necessary to hold galaxies together. Can your model re-create the Dark Matter map? http://www.scientificamerican.com/article.cfm?id=biggest-map-yet-of-universes Does it fit into any of the other known properties of Dark Matter? Also also, tossing out virtual photons, you leave an awful lot of conversation of energy & momentum issues. Does your idea also fix all that? Please demonstrate this, if yes.

Light is known not to need a medium, which has been called 'the luminiferous aether'. The Michael-Morley experiment, and all the subsequent follow up experiments, pretty conclusively proved that light does not require a medium. See http://en.wikipedia.org/wiki/Michelson–Morley_experiment to begin with. No aether has ever been found despite many different experiments to attempt to find one. How do you reconcile your idea with this experimental result?

If this is really how it is being done, can you please provide a citation for it? I am interested to read more if it is out there.

1) that is not what you started out saying. You were using phrases like "only possible way", and insisting that you needed 6 pieces of data to location a point in space. 2) just how often does it happen that you don't know the location of a certain object, but you do know the locations of 6 other objects, 3 pairs of which just happen to be co-linear with the unknown object? Your method certainly can work, I just don't see how it is very practical at all. The chances of just having 3 pairs of co-linear objects is going to be incredibly rare, and if this is a region of space you don't know something as basic as distances, I think that the chances of knowing the distances of the surround objects is also unlikely.

I am just curious... if this is the only possible way to locate the position of objects, how did mankind ever figure out the locations of anything off of the earth? For example, the Earth's moon? We didn't have 6 other objects of known position to draw lines that intersect at the moon's position, so how could that ever be known? In other words, how could this procedure ever be started? And ask yourself, how we know that Alpha Centauri is about 4.37 light years from our sun?

Nope. I am not spending my money to buy your book. Frankly, it is no skin off my teeth whether you decide to defend your idea or not. I'm not the one that comes off looking foolish by refusing to provide asked information.

Go and open any statics textbook. How deep into that book do you think you are going to get until you have an equation. Newton's First Law at its heart is a math equation. For example, repeated application of Newton's First Law helps find the forces a strut in a bridge experience. The new force on that strut is zero (assuming the bridge ins't collapsing), but words alone cannot tell me what forces that strut will experience and hence what it needs to be made out of.

I frankly don't care what tools you choose to use. Science wants to see your tools make predictions and then compare those predictions to measurements. If your tools can do that, then science away. Metaphysics to date hasn't demonstrated a lot of usefulness in that, but I am open minded enough that if someone can demonstrate how metaphysics can make objective clear-cut predictions, then I am all ears. I think you really need to quantify 'most' here. I suspect you'll find that there are an awful lot of incremental advancements than these giant leaps than you think. In other words, please present evidence that supports the use of the word 'most'. Intuition and all that is fine. But, all the way back to my first post, it still takes a lot of work to make that intuition meaningful. Because one has to learn what is and isn't meaningful in the context of the problem. And intuition alone can be very, very misleading. There is quite a lot about physics that can be downright un-intuitive. One develops a good physics intuition by actually studying physics. And that's where the math comes in. This is another of my favorite soliloquies on here, but i'm going to repeat it again. Words can be great. There are numerous absolute classics of literature written in just words. But part of what makes so many of them great is how they are constantly reinterpreted and reevaluated in different contexts. In no small part of that is because words have different meanings to different people. This is why math is so, so, so very preferred. Here is a favorite example. If I walked into the room you are in carrying a box, put that box down in front of you and said "that box is heavy." what does that really mean? What is I were an Olympic weightlifter? What if I were a ballet dancer? And so on. Now, what if I said "the force of gravity on this box is 500 N." That leaves no room for interpretation. That box is not 50 N, not 5000 N. But 500 N. Whether I, you, or anyone else can lift 500 N with ease, or really struggle with it. There are no words that so exactly convey the information that the force of gravity on that box is 500 N. How this relates is that if I have two models, model A predicts the box will feel 38.5 N of force due to gravity and model B predicts the box will experience 502.1 N of force, clearly one of those models is superior to another. I can actually measure how much error exists between the predictions and the measurements. And someday, if an even better model comes along and predicts 499.89 N, then it will displace the other model. If you can write words to replicate the exactness of the above, I would be very interested to see it. No one has been able to meet that challenge all the other times I've presented it. In other words, the math gives you those objective, clear-cut, non-fungible predictions. Words will never be that precise. The words can be the beginning of a good idea. But, words alone are almost never science. Science is making accurate predictions about measurements. The language of that is mathematics. Lastly, there is no shame in not personally being able to do the mathematics. Each of us have different strengths and weaknesses. But know that without the mathematics, it will be of very limited use to science. That is just how it is. In the same way, I will never be able to make anything useful to the communities of ballet dancing, Olympic weightlifting, and numerous other communities. That is just how it is.

A sine wave is only symmetric at its peaks and troughs. Not over the entire curve. There is a very significant difference between [math]\sin(x - ct)[/math] and [math]\sin(x + ct)[/math]. I'm sorry, I may be the 'maths expert' here (what I will readily admit is overall a rather meaningless title the forum's administers decided to give me), but this is an idea you are defending. Why can't you actually post the math and demonstrate to me, to everyone that the change in the direction doesn't actually change the final results. Can you do that? Or is this all just wordsmithing and handwaving? Show us all wrong by posting it and demonstrating what you claim.

This sentiment is actually something I do post very often, too. That the simple truth is that the vast, vast majority of our current scientific understanding is wrong, or at the very least, fairly incomplete. This is just simple because we know that there are still open problems out there. But, what we do know today will still be known tomorrow. Any future models will incorporate known results from today. I still think that the best example is the improvement from Newtonian mechanics to special and general relativity. In short, Newtonian mechanics is amazingly accurate in the right domains. So long as objects aren't too massive, or moving too fast, or too small, Newtonian mechanics are an excellent fit to what we actually see. The theories of relativity did a good job fixing up the issues for the vary large scale things. Now, here's the real kicker. If you take the equations of GR, and put into them masses that aren't moving anywhere near the speed of light, and masses that are pretty small (on a solar scale)... do you know what you get back out? Newtonian mechanics. The exact same tried and true equations. So, in short, the improvement completely absorbed the original models. The improvement made predictions that also agreed with the predictions made by the old models that were very good under the correct circumstances. This is why it is so so so very important that current good models are known by anyone who wants to replace them. Any new model must replicate the successes of the current model. This is undeniable unless there is good evidence that someone has been falsifying data (a rare occurrence, but unfortunately something that has happened before). This is a point that so many speculators miss. That is there is a data point out there, their supposedly better model had better be able to predict it better than the current models, otherwise why would anyone abandon the current model for a worse one? The advances in science has often led to existing data being re-evaluated, re-interpreted, or re-examined. But it doesn't destroy old data. So, the next person who comes here and hates quantum mechanics (a particular favorite for the speculators to want to replace) that is fine... but the current predictions and experiments are out there to stay. They had better show how their idea makes predictions at least as good as QM today. And if their idea has an inconsistency with known data today... that is a giant problem. That is something that can't just be wished away or ignored. This is why it is so important to know the current theories and the current data. Without this knowledge, it is very hard to discuss the idea, isn't it? And all that is besides the fact that there is so much science that is built on the shoulders of giants before them. I don't understand the proclivity to ignore this slice of all the great minds that came before us today, too, but that is another topic for another day.