weights in multiplication
$begingroup$
I understand I have an equation of two fractions $0 < A, B < 1$.
$$R = dfrac{A+B}2,$$ where $0 < R, A, B <1$. I understand how to bias towards A or B by inserting weights k1,k2 to the equation, like
$$R = dfrac{k_1A + k_2B}2,$$ where $k_1+k_2=1$.
how do I bias towards a variable in a multiplication scenario, i.e., $R = AB$ ? I sense it should be something like $R = (A+k_1)(B+k_2)$ but I cannot find the boundary conditions and relationship for $k_1,k_2$. Would appreciate your help!
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th.
So A*B should be 1/49 (or as close as possible)
Thanks,
Andreas
calculus
$endgroup$
add a comment |
$begingroup$
I understand I have an equation of two fractions $0 < A, B < 1$.
$$R = dfrac{A+B}2,$$ where $0 < R, A, B <1$. I understand how to bias towards A or B by inserting weights k1,k2 to the equation, like
$$R = dfrac{k_1A + k_2B}2,$$ where $k_1+k_2=1$.
how do I bias towards a variable in a multiplication scenario, i.e., $R = AB$ ? I sense it should be something like $R = (A+k_1)(B+k_2)$ but I cannot find the boundary conditions and relationship for $k_1,k_2$. Would appreciate your help!
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th.
So A*B should be 1/49 (or as close as possible)
Thanks,
Andreas
calculus
$endgroup$
$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55
add a comment |
$begingroup$
I understand I have an equation of two fractions $0 < A, B < 1$.
$$R = dfrac{A+B}2,$$ where $0 < R, A, B <1$. I understand how to bias towards A or B by inserting weights k1,k2 to the equation, like
$$R = dfrac{k_1A + k_2B}2,$$ where $k_1+k_2=1$.
how do I bias towards a variable in a multiplication scenario, i.e., $R = AB$ ? I sense it should be something like $R = (A+k_1)(B+k_2)$ but I cannot find the boundary conditions and relationship for $k_1,k_2$. Would appreciate your help!
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th.
So A*B should be 1/49 (or as close as possible)
Thanks,
Andreas
calculus
$endgroup$
I understand I have an equation of two fractions $0 < A, B < 1$.
$$R = dfrac{A+B}2,$$ where $0 < R, A, B <1$. I understand how to bias towards A or B by inserting weights k1,k2 to the equation, like
$$R = dfrac{k_1A + k_2B}2,$$ where $k_1+k_2=1$.
how do I bias towards a variable in a multiplication scenario, i.e., $R = AB$ ? I sense it should be something like $R = (A+k_1)(B+k_2)$ but I cannot find the boundary conditions and relationship for $k_1,k_2$. Would appreciate your help!
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th.
So A*B should be 1/49 (or as close as possible)
Thanks,
Andreas
calculus
calculus
edited Dec 26 '18 at 15:42
Andreas Prs
asked Dec 26 '18 at 10:00
Andreas PrsAndreas Prs
11
11
$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55
add a comment |
$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55
$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
From your first example, it looks like you want a formula for $R = f(k,A,B)$ where $lim_{k to 1} R = A$, and $lim_{k to 0} R = B$. The following is one possibility: $R = A^k B^{1-k}$, but I am not sure if this will behave as you want for any $0<k<1$.
Since R is the mean of A,B in your first example, perhaps you are looking for a weighted geometric mean?
$R = e^{k ln(A)+(1-k)ln(B)}$
$endgroup$
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
|
show 1 more comment
$begingroup$
Because you started your question by asking about a sum which is really an arithmetic mean, people who responded have tended to interpret your "multiplication" question as a question about the geometric mean,
because the geometric mean has the same relationship to multiplication that the arithmetic mean has to addition.
For a weighted geometric mean, you would choose $k_1$ and $k_2$ such that
$k_1+k_2=1,$ $0 leq k_1 leq 1,$ and $0 leq k_2 leq 1,$ and then the weighted geometric mean is
$$ R=A^{k_1}B^{k_2}.$$
Note that I wrote more conditions on $k_1$ and $k_2$ than you really need; in effect, I wrote four inequalities (two for $k_1,$ two for $k_2$) where only two are actually needed. For example, $0 leq k_1$ and $0 leq k_2$ would have been enough.
Alternatively, with the same conditions on $k_1$ and $k_2$ you could write
$$ R=A^{1 - k_1}B^{1 - k_2},$$
and this also would give a weighted geometric mean of the same kind, because the conditions imply that
$(1-k_1)+(1-k_2)=1,$ $0 leq (1-k_1) leq 1,$ and $0 leq (1-k_2) leq 1.$
The fact that $0 < R, A, B leq 1$ is not a problem. These formulas work for any positive numbers.
Also observe that when $k_1 = k_2 = frac12,$ you get the ordinary geometric mean,
$$ R = A^{1/2}B^{1/2} = sqrt{AB}.$$
If you actually want a weighted product, so that equal weighting would give you
$R = AB,$ then you can simply double the exponents in the geometric mean.
For example, you can set
$$ R=A^{2k_1}B^{2k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = frac12.$
If you would prefer not to write the $2$ in each exponent, you can change the conditions so that $k_1+k_2=2,$ $0 leq k_1 leq 2,$ and $0 leq k_2 leq 2,$
and write
$$ R=A^{k_1}B^{k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = 1$ and otherwise gives you a product weighted toward whichever factor has the greater exponent.
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
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active
oldest
votes
$begingroup$
From your first example, it looks like you want a formula for $R = f(k,A,B)$ where $lim_{k to 1} R = A$, and $lim_{k to 0} R = B$. The following is one possibility: $R = A^k B^{1-k}$, but I am not sure if this will behave as you want for any $0<k<1$.
Since R is the mean of A,B in your first example, perhaps you are looking for a weighted geometric mean?
$R = e^{k ln(A)+(1-k)ln(B)}$
$endgroup$
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
|
show 1 more comment
$begingroup$
From your first example, it looks like you want a formula for $R = f(k,A,B)$ where $lim_{k to 1} R = A$, and $lim_{k to 0} R = B$. The following is one possibility: $R = A^k B^{1-k}$, but I am not sure if this will behave as you want for any $0<k<1$.
Since R is the mean of A,B in your first example, perhaps you are looking for a weighted geometric mean?
$R = e^{k ln(A)+(1-k)ln(B)}$
$endgroup$
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
|
show 1 more comment
$begingroup$
From your first example, it looks like you want a formula for $R = f(k,A,B)$ where $lim_{k to 1} R = A$, and $lim_{k to 0} R = B$. The following is one possibility: $R = A^k B^{1-k}$, but I am not sure if this will behave as you want for any $0<k<1$.
Since R is the mean of A,B in your first example, perhaps you are looking for a weighted geometric mean?
$R = e^{k ln(A)+(1-k)ln(B)}$
$endgroup$
From your first example, it looks like you want a formula for $R = f(k,A,B)$ where $lim_{k to 1} R = A$, and $lim_{k to 0} R = B$. The following is one possibility: $R = A^k B^{1-k}$, but I am not sure if this will behave as you want for any $0<k<1$.
Since R is the mean of A,B in your first example, perhaps you are looking for a weighted geometric mean?
$R = e^{k ln(A)+(1-k)ln(B)}$
answered Dec 26 '18 at 12:12
SamadinSamadin
42117
42117
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
|
show 1 more comment
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Hi, so you propose instead of using two weights just use one, and find the neutral solution (bias-wise) at k=0.5? So k=0.6 biases towards B and k=0.4 biases towards A?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:24
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Yup. Sticking with $k_2=1-k_1$ I just wrote $k$ and $1-k$ instead of $k_1$ and $k_2$, same thing.
$endgroup$
– Samadin
Dec 26 '18 at 12:52
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Note for k=0.5, this doesn't give you back AB. So you'd have to do some tweaking.
$endgroup$
– Samadin
Dec 26 '18 at 12:56
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
Thanks a lot for your reply! The weighted mean does not give me AB rather than the mean of the two values for k=0.5, right? What do you mean by tweaking? On the weighted geometric mean or for the R=A^k*B^k ? I would need an equation that is close to that product when k=0.5, but I am not sure of how to approach this.
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:26
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
$begingroup$
By the way my range for A, B is 1/7 -> 1, with a step of 1/7th. So A*B should be 1/49 (or as close as possible)
$endgroup$
– Andreas Prs
Dec 26 '18 at 15:42
|
show 1 more comment
$begingroup$
Because you started your question by asking about a sum which is really an arithmetic mean, people who responded have tended to interpret your "multiplication" question as a question about the geometric mean,
because the geometric mean has the same relationship to multiplication that the arithmetic mean has to addition.
For a weighted geometric mean, you would choose $k_1$ and $k_2$ such that
$k_1+k_2=1,$ $0 leq k_1 leq 1,$ and $0 leq k_2 leq 1,$ and then the weighted geometric mean is
$$ R=A^{k_1}B^{k_2}.$$
Note that I wrote more conditions on $k_1$ and $k_2$ than you really need; in effect, I wrote four inequalities (two for $k_1,$ two for $k_2$) where only two are actually needed. For example, $0 leq k_1$ and $0 leq k_2$ would have been enough.
Alternatively, with the same conditions on $k_1$ and $k_2$ you could write
$$ R=A^{1 - k_1}B^{1 - k_2},$$
and this also would give a weighted geometric mean of the same kind, because the conditions imply that
$(1-k_1)+(1-k_2)=1,$ $0 leq (1-k_1) leq 1,$ and $0 leq (1-k_2) leq 1.$
The fact that $0 < R, A, B leq 1$ is not a problem. These formulas work for any positive numbers.
Also observe that when $k_1 = k_2 = frac12,$ you get the ordinary geometric mean,
$$ R = A^{1/2}B^{1/2} = sqrt{AB}.$$
If you actually want a weighted product, so that equal weighting would give you
$R = AB,$ then you can simply double the exponents in the geometric mean.
For example, you can set
$$ R=A^{2k_1}B^{2k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = frac12.$
If you would prefer not to write the $2$ in each exponent, you can change the conditions so that $k_1+k_2=2,$ $0 leq k_1 leq 2,$ and $0 leq k_2 leq 2,$
and write
$$ R=A^{k_1}B^{k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = 1$ and otherwise gives you a product weighted toward whichever factor has the greater exponent.
$endgroup$
add a comment |
$begingroup$
Because you started your question by asking about a sum which is really an arithmetic mean, people who responded have tended to interpret your "multiplication" question as a question about the geometric mean,
because the geometric mean has the same relationship to multiplication that the arithmetic mean has to addition.
For a weighted geometric mean, you would choose $k_1$ and $k_2$ such that
$k_1+k_2=1,$ $0 leq k_1 leq 1,$ and $0 leq k_2 leq 1,$ and then the weighted geometric mean is
$$ R=A^{k_1}B^{k_2}.$$
Note that I wrote more conditions on $k_1$ and $k_2$ than you really need; in effect, I wrote four inequalities (two for $k_1,$ two for $k_2$) where only two are actually needed. For example, $0 leq k_1$ and $0 leq k_2$ would have been enough.
Alternatively, with the same conditions on $k_1$ and $k_2$ you could write
$$ R=A^{1 - k_1}B^{1 - k_2},$$
and this also would give a weighted geometric mean of the same kind, because the conditions imply that
$(1-k_1)+(1-k_2)=1,$ $0 leq (1-k_1) leq 1,$ and $0 leq (1-k_2) leq 1.$
The fact that $0 < R, A, B leq 1$ is not a problem. These formulas work for any positive numbers.
Also observe that when $k_1 = k_2 = frac12,$ you get the ordinary geometric mean,
$$ R = A^{1/2}B^{1/2} = sqrt{AB}.$$
If you actually want a weighted product, so that equal weighting would give you
$R = AB,$ then you can simply double the exponents in the geometric mean.
For example, you can set
$$ R=A^{2k_1}B^{2k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = frac12.$
If you would prefer not to write the $2$ in each exponent, you can change the conditions so that $k_1+k_2=2,$ $0 leq k_1 leq 2,$ and $0 leq k_2 leq 2,$
and write
$$ R=A^{k_1}B^{k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = 1$ and otherwise gives you a product weighted toward whichever factor has the greater exponent.
$endgroup$
add a comment |
$begingroup$
Because you started your question by asking about a sum which is really an arithmetic mean, people who responded have tended to interpret your "multiplication" question as a question about the geometric mean,
because the geometric mean has the same relationship to multiplication that the arithmetic mean has to addition.
For a weighted geometric mean, you would choose $k_1$ and $k_2$ such that
$k_1+k_2=1,$ $0 leq k_1 leq 1,$ and $0 leq k_2 leq 1,$ and then the weighted geometric mean is
$$ R=A^{k_1}B^{k_2}.$$
Note that I wrote more conditions on $k_1$ and $k_2$ than you really need; in effect, I wrote four inequalities (two for $k_1,$ two for $k_2$) where only two are actually needed. For example, $0 leq k_1$ and $0 leq k_2$ would have been enough.
Alternatively, with the same conditions on $k_1$ and $k_2$ you could write
$$ R=A^{1 - k_1}B^{1 - k_2},$$
and this also would give a weighted geometric mean of the same kind, because the conditions imply that
$(1-k_1)+(1-k_2)=1,$ $0 leq (1-k_1) leq 1,$ and $0 leq (1-k_2) leq 1.$
The fact that $0 < R, A, B leq 1$ is not a problem. These formulas work for any positive numbers.
Also observe that when $k_1 = k_2 = frac12,$ you get the ordinary geometric mean,
$$ R = A^{1/2}B^{1/2} = sqrt{AB}.$$
If you actually want a weighted product, so that equal weighting would give you
$R = AB,$ then you can simply double the exponents in the geometric mean.
For example, you can set
$$ R=A^{2k_1}B^{2k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = frac12.$
If you would prefer not to write the $2$ in each exponent, you can change the conditions so that $k_1+k_2=2,$ $0 leq k_1 leq 2,$ and $0 leq k_2 leq 2,$
and write
$$ R=A^{k_1}B^{k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = 1$ and otherwise gives you a product weighted toward whichever factor has the greater exponent.
$endgroup$
Because you started your question by asking about a sum which is really an arithmetic mean, people who responded have tended to interpret your "multiplication" question as a question about the geometric mean,
because the geometric mean has the same relationship to multiplication that the arithmetic mean has to addition.
For a weighted geometric mean, you would choose $k_1$ and $k_2$ such that
$k_1+k_2=1,$ $0 leq k_1 leq 1,$ and $0 leq k_2 leq 1,$ and then the weighted geometric mean is
$$ R=A^{k_1}B^{k_2}.$$
Note that I wrote more conditions on $k_1$ and $k_2$ than you really need; in effect, I wrote four inequalities (two for $k_1,$ two for $k_2$) where only two are actually needed. For example, $0 leq k_1$ and $0 leq k_2$ would have been enough.
Alternatively, with the same conditions on $k_1$ and $k_2$ you could write
$$ R=A^{1 - k_1}B^{1 - k_2},$$
and this also would give a weighted geometric mean of the same kind, because the conditions imply that
$(1-k_1)+(1-k_2)=1,$ $0 leq (1-k_1) leq 1,$ and $0 leq (1-k_2) leq 1.$
The fact that $0 < R, A, B leq 1$ is not a problem. These formulas work for any positive numbers.
Also observe that when $k_1 = k_2 = frac12,$ you get the ordinary geometric mean,
$$ R = A^{1/2}B^{1/2} = sqrt{AB}.$$
If you actually want a weighted product, so that equal weighting would give you
$R = AB,$ then you can simply double the exponents in the geometric mean.
For example, you can set
$$ R=A^{2k_1}B^{2k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = frac12.$
If you would prefer not to write the $2$ in each exponent, you can change the conditions so that $k_1+k_2=2,$ $0 leq k_1 leq 2,$ and $0 leq k_2 leq 2,$
and write
$$ R=A^{k_1}B^{k_2},$$
which gives you $R = AB$ when $k_1 = k_2 = 1$ and otherwise gives you a product weighted toward whichever factor has the greater exponent.
answered Dec 26 '18 at 19:53
David KDavid K
55.1k344120
55.1k344120
add a comment |
add a comment |
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$begingroup$
Not too sure what you are asking. Do you have any context for this? I guess you can try $$R=A^{k_1}B^{k_2}$$ with $k_1+k_2=1$.
$endgroup$
– Karn Watcharasupat
Dec 26 '18 at 11:59
$begingroup$
Hi, its 0< R, A, B <1 , so this solution cannot be applied. Would I be looking at something like R = A^{1-k1}B^{1-k2} ?
$endgroup$
– Andreas Prs
Dec 26 '18 at 12:26
$begingroup$
The weighted arithmetic mean is simply $R = k_1A + k_2B,$ without dividing by $2.$ The equation $R = frac{A+B}2$ then is what you get when $k_1=k_2=frac12.$
$endgroup$
– David K
Dec 26 '18 at 19:55