RE: AI-GEOSTATS: Re: Lagrange Multiplier
On 11-Oct-06 Njeri Wabiri wrote: Dear list Just a newbabie question What is the statistical interpretation of the Lagrange multiplier in kriging. At least I know if its positive we have a high kriging variance and vice versa. Grateful for a response and a reference Njeri The general interpretation of a Langrange multiplier is as follows. In the context of an extremal problem (find the max/min) of a function f(x1,x2,...,xk) subject to a constraint g(x1,x2,...,xk) = c when you express it as solving the extremal problem for f(x1,x2,...,xk) - L*g(x1,x2,...,xk) (and then using the constraint equation to eliminate L), the value of L is equal to the rate of change of the extremal value of f (as so found) with respect to variation in the value of c. Otherwise put: for a particular value of c, the extremal value of f is (say) fmax(c). Then L = dfmax(c)/dc. The same is true with several constraints (and a corresponding number of lagrange multipliers), in that the i-th L is equal to the partial derivative of fmax(c1,c2,...,cr) with respect to ci. [This assumes that the function f has a max/min which can be found analytically by solving the equation[s] obtained by setting derivative[s] equal to 0. The above is not quite so directly true when the maximum is attained on the boundary of the region defined by the constraints, as in a Linear Programming problem, for instance; though something similar is also true there.] I can supply a demonstration of the above, if requested. So, if (in a statistical context) a sum of squares if minimised, or a likelihood is maximised, subject to constraints, then the above applies to the sum of squares, or likelihood, or whatever. Hoping this helps, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Oct-06 Time: 21:27:33 -- XFMail -- + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
RE: AI-GEOSTATS: Skewed Distributions
AI-GEOSTATS On 19-May-06 Digby Millikan wrote: Is it correct that the probability of being above or below the mean of a skewed distribution is not necessarily 0.5? Very much true that it may not be 0.5! As a general (though not absolutely true) rule, if a distribution is positively skewed then the mediam (0.5-point) is less than the mean, and for a negatively skewed distribution the median is greater than the mean. This often applies to the staddard distributions which turn up in practice. However, there is no general absolutely sure relationship between median and mean for non-symmetric distributions. Intuitively, the position of the median is independent of how you re-distribute the probability for values less than the median, and independent of how you re-distribute it for values greater than the median, so long as you keep 50% on either side. However, the mean is very much influenced by precisely where each bit of probability is located, so you can swing the mean around as much as you like without affecting the mediam. Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 19-May-06 Time: 15:43:57 -- XFMail -- + To post a message to the list, send it to ai-geostats@jrc.it + To unsubscribe, send email to majordomo@ jrc.it with no subject and unsubscribe ai-geostats in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
RE: [ai-geostats] Lagrange Multiplier
On 19-Sep-05 Colin Badenhorst wrote: Hi List, I've noticed that the latest version of my estimation software now stores the Lagrange multiplier (parameter) for each estimated block in my model. I was wondering if anyone had any thoughts on how I could use this variable in some practical sense to further interpret or validate my estimates. Unfortunately, my understanding of the Lagrange multiplier has always been poor, so I'm not sure exactly what it means. In a constrained optimisation problem, e.g. minimising a function y(x, y, ...) with constraints expressed in the form g(x, y, ...) = c the value of the Lagrange multiplier associated with constraint g(x, y, ...) = c can be interpreted as the rate of change of the optimum value of f(x, y, ... ) with respect to change in c. Otherwise put, if say c1, c2, ... are the values of the constraint functions, the minimisation of f(x, y, ... ) will lead to a particular value of f() which will depend on the values of c1, c2, ... which were used, so it is a function fmin(c1, c2, ... ) of these. If U1, U2, ... are the Lagrange multipliers in the Lagrangean formulation of the problem, then U1 = dfmin(c1, c2, ... )/dc1 and similarly for U2 etc. Does this help? Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 19-Sep-05 Time: 12:33:34 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] A banal question...
of giving you quite a lot about the detailed behaviour of the terrain. In particular it could probably be applied to help determine the overall hydrography of the region -- e.g. what complexity of drainage systems would you need. However, as well as the fact that the informativeness of C(h) depends on properties like stationarity and isotropy, also a lot of theory about analysing measurements on spatial processes depends on assuming these properties in order to make progress. The fundamental issue that depends on these assumtions is the question: whether the expectation of a random variable at an arbitrary point will be the same as the average over several fixed points. In the mathematical theory, it would be assumed that these held to an indefinite distance in all directions. In practice, it is often adequate that they should hold for a sufficient distance, which is beyind the range at which C(h) falls to small values. So if I were only concerned to draw conclusions about what happens within 5km of where I live, I would not worry about the fact that it all fell apart 20kn away! I hope this contributes further to clarifying your query! best wishes, Ted. [**] There is a potential source of anisotopy: The region is intersected by a number of watercourses -- on the size-scale of rivers (which some of them are) -- which are contained within raised banks (2-3m high), each of which tends to run in a straight line for several km. Therefore at certain points, given that the height is 2m or more, it will remain so for a considerable distance in a particular direction. Therefore the assumption of stationarity and isotropy do not hold strictly everywhere. However, the proportion of the area over which they do not hold is a very small fraction of the whole. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 02-May-05 Time: 18:08:16 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] A matter of pronunciation
On 11-Apr-05 Glover, Tim wrote: Here's a somewhat esoteric question for those of you who've had direct contact with the early giants in the field (on whose shoulders we now stand): What is the correct pronunciation of kriging? I've heard it CREE-ging, CRIG - ing, CREE-jing, etc. Without such direct contact, I think one can nevetheless answer by appeal to first principles. Kriging was named after Krige, a South African mining engineer. As such, his name would have been pronounced approximately Dutch-fashion, where the g is a kind of throat-clearing sound, perhaps most practically approximated for English speech by simply using the standard hard g. The i, however, would have been between the i in pick and the ee in peek, so if we denote this by î then perhaps the best way to pronounce the word would be Krîging (with a hard g), i.e. between CRIG-ing and CREE-ging Awaiting the truth from those who really know, Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 11-Apr-05 Time: 15:43:05 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] Probability distribution of a moving object be
On 21-Feb-05 Sunny Elspeth Townsend wrote: Dear list members, I would like to know how to find the probability distribution for the position of an object moving between two known points. I am trying to reconstruct the tracks of fishing vessel using satellite data which gives the ship's position every 2 hours. The ships do not move often move in straight lines, and I want to be able to incorporate the uncertainty of where the ship coulf be between the known points. I have already found the outer limit of movement which takes the form of an ellipse with the start and end points as the foci (for this I assumed constant speed). My question is: What is the probability distribution of the object within the ellipse. I would like to find the statistical distribution but would be happy if anyone knows if any GIS software has a function for this. Please reply to [EMAIL PROTECTED] Thank you for any help. -- Sunny Elspeth Townsend [EMAIL PROTECTED] You are asking an undefined question here! Your ellipses are calculated on the basis of constant speed in a straight line from one determined point, out to some unknown point, and then at constant speed from there to the next determined point. Therefore the sum of the two distances travelled is constant, and the result, as you say, is an ellipse with the two determined points as foci. There is an implicit assumption of what this constant speed is, since you need this to work out what total distance is travelled, and you do not state what this assumption is based on. But in any case there will in real life be, between the two determined points, variations in speed (relative to the water) and of direction, and further variations in absolute speed and direction due to currents. Some of these will be random -- due to external influences such as wind -- and some will be the result of deliberate choices (which are also likely to be influenced by external factors, such as locating a shoal of fish which could cause the vessel to linger in that area, which themselves have a random character). The vessel may be following various policies, e.g. a) basically trying to sail in a straight line at constant speed in order to get from A to B b) zig-zagging haphazardly over an area in order to try to locate fish c) pursuing a systematic sweep over an area on the lines of 5km E, 200m N, 5km W, 200m N, 5km E, ... The real probability distribution will depend on how all these factors combine probabilistically. You cannot expect any software to have a function for this unless you are able to supply information about such factors. You cannot expect the software to guess it for you. One view of how to approach this kind of question would assume a kind of random walk or diffusion with drift: There is an overall tendency to move in a cerain direction, but at frequent moments of time there are random changes of direction and possibly also speed. Starting from A, there will (subject to explicit assumptions about these random changes) be a probability distribution of position after a given time. The fact that B is a determined point at a determined time will impose a condition on this distribution, from which the conditional probability distribution of position at any intermediate time can be determined (though not necessarily easily). In the case of diffusion according to Brownian motion this is known to probabilists as the Brownian Bridge. Such approaches have been applied to probabilistic study of (e.g.) bird migration, on which there is quite a large literature. However, no such considerations allow you to escape from the necessity of thinking realistically about what variations from uniform motion in a straight line are likely to occur, and about what random laws they may follow. Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 Date: 21-Feb-05 Time: 10:29:34 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] F and T-test for samples drawn from the same p
On 03-Dec-04 Colin Badenhorst wrote: Hello everyone, I have two groups of several thousand samples analysed for various elements, and wish to determine if these samples are drawn from the same statistical population for later variography studies. I propose to test the two groups by using a F-test to test the sample variances, and a T-test to test the group means, at a given confidence limit. Before I do this, I wonder how I would interpret the results of the test if, for example: 1. The F-test suggests no significant statistical difference between the variances at a 90% confidence limit, BUT 2. The T-test suggests a significant statistical difference between the means at the same, or lower confidence limit. Has anyone come across this scenario before and how are they interpreted? On the face of it, the scenario you describe corresponds to a standard t-test (which involves an assumption that the variances of the two populations do not differ), though I'm not sure what you mean in (2) by significant at the same, or lower confidence limit. (Do I take it that in (1) you mean that the P-value for the F test is 0.1 or less?) However, if you get significant difference between the variances in (1), then it may not be very good to use the standard t test (depending on how different they are). A modified version, such as the Welch test, should be used instead. There is an issue with interpreting the results where the samples have initially been screened by one test, before another one is applied, since the sampling distribution of the second test, conditional on the outcome of the first, may not be the same as the sampling distribution of the second test on its own. However, I feel inclined to guess that this may not make any important difference in your case. Hoping this helps, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 094 0861 [NB: New number!] Date: 03-Dec-04 Time: 14:15:09 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: [ai-geostats] FW: spatial relationships
On 02-Sep-04 Glover, Tim wrote: Thisa reminds me of a site where the failure of variogram modeling actually told me quite a bit about the problem at hand. It was a large field where dumptruck loads of soil with a contaminant had been dumped randomly and spread. This was unknown until after a gridded set of samples had been taken and a bizarre spotted pattern emerged. The directional variogram showed an unusual hump - increasing variance with distance, then decreasing variance with even more distance. This was the clue that some sort of spot activity had occurred. We finally tracked down a retired ex-employee who remembered the dumping activity. Sometimes a failed model tells more than one that fits! Indeed! It's the difference between discovery and measurement. Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 02-Sep-04 Time: 14:19:37 -- XFMail -- * By using the ai-geostats mailing list you agree to follow its rules ( see http://www.ai-geostats.org/help_ai-geostats.htm ) * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED] Signoff ai-geostats
RE: AI-GEOSTATS: Naive question on lat-long conventions
On 15-Apr-04 Mark Coleman wrote: ... In particular, I have been using a small test data set provided by Anselin on crime data in Columbus OH. Each sample point includes a lat-long coordinate. The data set can be found at http://www.spatial-econometrics.com/data/ As an example, the first point in this data set shows latitude=35.62, longitude=42.38. Yet when I check the coordinates of a random point in Columbus OH (not in the dataset), I find coordinates in the range of Lat=40.x, Long= -82.y. Well, clearly either there's something seriously wrong with the data (35.62 deg N 42.38 deg W is in the middle of the Atlantic, 35.62 deg N 42.38 deg E is on the Iraq-Syria border) or things are different from what one might first think they are. According to the file http://www.spatial-econometrics.com/data/anselin.txt we are only told that % column4 = latitude coordinate % column5 = longitude coordinate but we are not told the units nor the origin. Have a look at the numbers in http://www.spatial-econometrics.com/data/anselin.dat min(lat) = 25.25, max(lat) = 51.24 min(long) = 24.95, max(long) = 44.07 Now, 1 degree of latitude is about 60 miles, so if these were degrees then you're looking at a range of some 1,500 miles N-S; similarly (though less) for E-W. Even Ohio ain't that big! So I suspect that we're looking at minutes of arc rather than degrees, relative to some fixed point. One minute of arc in longitude is about 1 mile, so you're now looking at about 26 miles N-S. At the latitude of Columbus (35.62N), 1 minute of arc is about 0.66 miles, so you're looking at about 11 miles E-W. Also, it seems that the precision is 0.01 minutes of arc, or about 18 yards (N-S) by 12 yards (E-W). This all looks plausible ... Hoping this helps (and wondering if I'm right ... pity people don't document this sort of thing properly in their data files; it would only have taken a line to do so.) Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 15-Apr-04 Time: 17:52:40 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
RE: AI-GEOSTATS: Hypothesis Testing in a Spatial Framework
On 19-Dec-03 Warren Schlechte wrote: I would like some references for the following type design. Consider an area, much of which will not be treated. However, small sections will be treated, and if the treatment works, the density of an organism will be lessened. The basic question is What is the likelihood that the low-density areas would have happened by chance, in the absence of the treatment? If this likelihood is very small, then we could suggest the treatment is the reason we have lower densities in these areas. Note, there could be a gradient with edge effects. How would one go about making a decision rule for testing whether the treatment was effective? My guess is that the non-treated areas will be quite patchy. Our hope is that in the treated areas, if the organism isn't completely eliminated, the density will be considerably less. [...] Warren Schlechte HOH Fisheries Science Center The issues you raise occur also in classical agricultural field trials, and you might like to consider using classical field experiment techniques. For instance, you could select a set of what you call sections as plots, and randomly allocate half the plots to treatment/control. Then, if you are mainly interested in a hypothesis-test type of inference (which your statement suggests is the case), the randomisation distribution of treatment/control comparison would give you a valid result. If you can select well-separated plots so that they do not interfere with each other, this may be all you need. If gradients are a serious concern, you could make your experiment more sensitive by identifying strata to use as blocks, within each of which you expect to find less variation due to this cause. Then you randomise on plots chosen within each block, and again make use of the appropriate randomisation distribution. However, if you need to make a more quantitative inference, such as producing estimates with confidence intervals (or even merely doing a t or F test), then you may need to take account of possible spatio/temporal correlations on order to estimate the variances of your estimates, and this is not so easy. Again, there is a good deal of literature about this in the domain of agricultural experimentation. Given where you are writing from, it seems you may be looking at bodies of water. You do not say whether these are rivers/canals (approximately linear structures), or lakes or seas (2, or even 3, dimensional). This would affect the type of layout you would choose. In canals or lakes, the water may be relatively static, so the treatment is more likely to remain within the plot, while in rivers or seas, the presence of currents may disperse it. The latter complication, as a serious issue, is rare in agricultural experiments. However, it may perhaps mainly influence your choice of procedure for measuring the concentration of the organism, though it could also severely complicate your estimation of variability. You do not say what the treatment is, by the way. I hope these thoughts help a bit. Best wishes, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 20-Dec-03 Time: 16:33:43 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
RE: AI-GEOSTATS: analysis of bone surfaces
On 24-Jun-03 ANN ZUMWALT wrote: I am a graduate student studying the functional morphology of bones. Part of my thesis entails characterizing the shape of a relatively complex 3D bone surface. I am testing to see whether exercise affects the morphology of this surface, so am looking for a way to test for differences between shapes/specimens. I am especially interested in testing for differences in the rugosity (ie, bumpiness) of the surfaces, but am interested in *any* method that would help me analyze these surfaces. I have 3D grid data (x,y,z) that represents the surfaces (I am scanning the bones with a 3D laser scanner to obtain this data). Can any of you suggest methods to analyze this data that will allow me to differentiate surfaces that are morphologically dissimilar? Hi Ann, Approaches would somewhat depend on the scale of the rugosity relative to the global curvature of the bone surface. If you can regard the surface as effectively flat (i.e. approximately a plane) then one aspect that could be revealing is the two-dimensional spectrum. However, if the scale of a bump is appreciable and the bone/specimen is curved, then you may have to fit a 2-D smooth surface to the overall shape of the bone, and then evaluate the rugosity say in terms of perpendicular distance from the fit. You would also then have the problem of defining position on the fitted surface in order to use say the spectrum. What do you mean by morphologically dissimilar? Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 24-Jun-03 Time: 20:30:23 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
RE: AI-GEOSTATS: generating 2-dim point pattern
On 10-Mar-03 Peter Bossew wrote:
I want to generate a set of points in a plane, {(xi,yi)}, such
that the points are distributed according to a chosen probability
distribution density f, i.e., the infinitesimal rectangle
[x,x+dx] X [y,y+dy] shall contain f(x,y)*dx*dy points.
How do I do this ??
In other words, I am looking for a generalization of the 1-dim method,
where this is done with F*(z), where F=cumul(f) and F* = inverse of F,
and z = random out of [0,1] (or any appropriate interval if f is not
normalized).
I tried to do it the same way, just using complex numbers, but this
does not seem to work, and could also not be generalized for n2
dimensions.
Hi Peter,
The problem with doing it the same way is that the same way only
applies in 1 dimension (as a consequence of the result that F(X) has
a uniform distribution, so that F*(X) has the distribution of X).
In more than 1 dimension, you still only have the result that F(X,Y)
has a uniform distribution, and this is only 1 equation. There is
no _straightforward_ way of basing it on two equations
(F1(X,Y), F2(X,Y)) = (Z1,Z1) where (Z1,Z2) is uniform on [0,1]x[0,1]
However, there are possible approaches. One is to use conditional
distributions: let F(X) be the marginal distribution for X, and let
G(Y;x) be the conditional distribution of Y given that X=x. Then
both U=F(X) and, for each x, V=G(Y;x) have uniform distributions,
so provided you can invert these as F*(U) and G*(V;x) you can then
sample U=u,V=v and obtain x=F*(u) and then y=G*(v;x).
The practical issues here depend a lot on the form of F(X,Y),
since both obtaining the conditional distribution and inverting
the functions F(X), G(Y;x) may be difficult in practice.
If it is straightforward to obtain the conditional distributions
for Y given X and X given Y, then Gibbs Sampling can enable you to
sample from (X,Y) without inverting the functions.
If you can't obtain the conditional distributions then it may be
impossible to follow such approaches, and then you may need to fall
back on simulating a random process with a known mechanism which has
the property of yielding (X,Y) distributed as f(x,y) -- if you know
of such a process!
If you could describe the density f(x,y) you are trying to sample
from to the list, then perhaps someone can suggest an approach.
(PS. sorry if it is trivial.)
No, it isn't trivial!
Best wishes,
Ted.
E-Mail: (Ted Harding) [EMAIL PROTECTED]
Fax-to-email: +44 (0)870 167 1972
Date: 10-Mar-03 Time: 08:29:19
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RE: AI-GEOSTATS: Summary: Extreme Values?
On 29-Dec-01 Myers, Jeff wrote: Ted's comments on the regulatory perspective bring up some interesting issues, assuming this were an hazardous waste site. Jeff, Thanks for your comments which are very much to the point. It's hard to contour yourself out of a situation you sampled yourself into. With your permission (which I assume will not be unreasonably withheld) I propose to trot out this delightful maxim on suitable occasions! Thanks for this too -- just in time to set me smiling for the New Year. Best wishes to all, Ted. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 167 1972 Date: 29-Dec-01 Time: 19:22:06 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and unsubscribe ai-geostats followed by end on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
Re: AI-GEOSTATS: Comments about software
On 12-Feb-01 Isobel Clark wrote: I agree that we do not want to turn the AI-Geostats list into an advertising free-for-all but it is very difficult not to refer to what you consider the perfect solution to a particular problem. Myself, I think this is fine as far as it goes. If Isobel knows that the software developed by her house does a particular job, and this may not be obvious or known to others, then I cannot see why there should be objection to her pointing it out in that context (otherwise the mickey-mouse situation could arise in which non-free software writers prompt their friends to point it out). I don't really see an essential difference between saying that E**SS* can do something specific, and saying that ARCI*** can do it. Equally, this should not take the form of an advertising blurb. Within the list policy of not allowing advertising, people should be able to mention a product with a decent restraint, to the extent justified by the context. I was unhappy about Gregoire's recent assertion that "it is a demo version of a commercial product and I can not accept to have it mentioned on the mailing list", since it carries an implication that no commercial product should be mentioned even in the context of pointing out that it may give the solution to a reader's problem. Surely it must be allowable to say that the solution to a problem can be found in XXX package, commercial or not? The delicate question of course is: where is the line that defines "decent restraint"? Ted. Topical Thought: It is better to arrive, than to travel hopefully. E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 284 7749 Date: 12-Feb-01 Time: 16:05:48 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
AI-GEOSTATS: Global Warming
Greetings! At the suggestion of Isobel Clark, I have just joined this list. Below is a message I already posted to the allstat mailing list, and Isobel has suggested I might get good responses from ai-geostats. Clearly, I have asked my question somewhat informally, but I think the intention is clear. I would be most obliged to hear of work directed towards a relative evaluation of two such hypotheses. With thank, and best wishes to all for the New Year. Ted. === I would be interested to learn of references to _data_ and to good discussions of such data which are relevant to a hypothesis of true global warming. By "true global warming" I mean in effect a sustained positive trend in the total heat content of the Earth's air and water (including major ice and snow masses and no doubt some allowance for the surface of the Earth itself). No doubt a good statement of what I mean would have to be more sophisticated, but you get the idea. The idea specifically being to consider also an alternative hypothesis whereby currently reported climatic changes, described as "global warming", may be manifestations of a redistribution of heat that is there already. A nice question in spatial statistics, I feel. With thanks in advance; interesting contributions will be summarised to the list. And, from globally warmed Manchester (-10 deg. C last night, allegedly), a Happy New Year to all. Ted. --End of forwarded message- -------- E-Mail: (Ted Harding) [EMAIL PROTECTED] Fax-to-email: +44 (0)870 284 7749 Date: 31-Dec-00 Time: 12:06:22 -- XFMail -- -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
