How to construct an example for the entropy equation: $H(Z)=H(X)+H(Y)$ where $Z=X+Y$ [duplicate]
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Entropy of sum is sum of entropies
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Given $Z=X+Y$ where X and Y are two random variables, under what conditions does $H(Z)=H(X)+H(Y)$?
Notice $Z$ is a function of $(X,Y)$, therefore $H(Z)leq H(X,Y)$, and $H(X,Y)leq H(X)+H(Y)-I(X;Y)$. Therefore when $Z$ and $(X,Y)$ is a one-to-one function and $X$ is independent of $Y$.
My question is, how to find an example that satisfy this problem ? I have a simple example that let $X=0$ so that $Z=Y$, which satisfy the conditions. However, can anyone gives a more non-trivial example ? Thanks.
information-theory entropy
marked as duplicate by leonbloy, user91500, Adrian Keister, Theoretical Economist, Brahadeesh Sep 12 at 17:31
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This question already has an answer here:
Entropy of sum is sum of entropies
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Given $Z=X+Y$ where X and Y are two random variables, under what conditions does $H(Z)=H(X)+H(Y)$?
Notice $Z$ is a function of $(X,Y)$, therefore $H(Z)leq H(X,Y)$, and $H(X,Y)leq H(X)+H(Y)-I(X;Y)$. Therefore when $Z$ and $(X,Y)$ is a one-to-one function and $X$ is independent of $Y$.
My question is, how to find an example that satisfy this problem ? I have a simple example that let $X=0$ so that $Z=Y$, which satisfy the conditions. However, can anyone gives a more non-trivial example ? Thanks.
information-theory entropy
marked as duplicate by leonbloy, user91500, Adrian Keister, Theoretical Economist, Brahadeesh Sep 12 at 17:31
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
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up vote
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This question already has an answer here:
Entropy of sum is sum of entropies
3 answers
Given $Z=X+Y$ where X and Y are two random variables, under what conditions does $H(Z)=H(X)+H(Y)$?
Notice $Z$ is a function of $(X,Y)$, therefore $H(Z)leq H(X,Y)$, and $H(X,Y)leq H(X)+H(Y)-I(X;Y)$. Therefore when $Z$ and $(X,Y)$ is a one-to-one function and $X$ is independent of $Y$.
My question is, how to find an example that satisfy this problem ? I have a simple example that let $X=0$ so that $Z=Y$, which satisfy the conditions. However, can anyone gives a more non-trivial example ? Thanks.
information-theory entropy
This question already has an answer here:
Entropy of sum is sum of entropies
3 answers
Given $Z=X+Y$ where X and Y are two random variables, under what conditions does $H(Z)=H(X)+H(Y)$?
Notice $Z$ is a function of $(X,Y)$, therefore $H(Z)leq H(X,Y)$, and $H(X,Y)leq H(X)+H(Y)-I(X;Y)$. Therefore when $Z$ and $(X,Y)$ is a one-to-one function and $X$ is independent of $Y$.
My question is, how to find an example that satisfy this problem ? I have a simple example that let $X=0$ so that $Z=Y$, which satisfy the conditions. However, can anyone gives a more non-trivial example ? Thanks.
This question already has an answer here:
Entropy of sum is sum of entropies
3 answers
information-theory entropy
information-theory entropy
asked Sep 8 at 10:22
SoManyProb_for_a_broken_heart.
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4881717
marked as duplicate by leonbloy, user91500, Adrian Keister, Theoretical Economist, Brahadeesh Sep 12 at 17:31
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
marked as duplicate by leonbloy, user91500, Adrian Keister, Theoretical Economist, Brahadeesh Sep 12 at 17:31
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To construct a proper example, as you found out yourself in a very nice fashion, there is a bijection between $Z$ and $(X,Y)$ (I suppose that random variables are discrete of course).
Let $X'sim textBernoulli(frac 12)$ and $Ysim textBernoulli(frac 12)$. Set $X=2X'$ and take $Z=X+Y$. Then $Z$ is uniformly distributed over $0,1,2,3$ and $X$ and $Y$ are also uniformly distributed over $0,2$ and $0,1$. Therefore it is easy to see that $H(Z)=H(X)+H(Y)$.
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
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In addition to the nice example @Arash gives. Assume the discrete uniform distribution. If $$X sim mathcal U_[a,b](n)$$and$$Y sim mathcal U_[c,d](m)$$
Let's define $$Z = X+Y$$
We know that the entropies of $X$ and $Y$ are $$H(X) = log n$$ and $$H(Y) = log m$$ therefore $$H(X) + H(Y) = log(n) + log(m) = log(nm)$$
This means that if $Z$ has $nm$ outcomes, then we can assert that $H(Z) = H(X) + H(Y)$. Many examples could be generated here to demonstrate the need of being bijective.
One Example out of MANY
Assume $X in lbrace 1,2,3rbrace$ and $Y in lbrace 0,10,20 rbrace$. It is easy to see that $$Z in lbrace 1,2,3,11,12,13,21,22,23 rbrace$$ with $$P(Z = z) = frac19$$
Is $H(Z) = ln(3times 3) = log 9$ ? Of course, because we $X+Y$ here is bijective.
Let's prove it: $$H(Z) = sumlimits_k=1^9 p_k log(frac1p_k) = sumlimits_k=1^9 frac19 log(9) =9frac19 log(9) = log(9)$$
Counter example
Now let's take the same distributions (with different outcomes) but show that $H(Z)$ will not reach $log(9)$ if there is no bijectivity. Let $X in lbrace 0,1,2 rbrace$ and $Y in lbrace 3,4,5 rbrace $. So $$Z in lbrace 3,4,5,6,7 rbrace$$
Therefore, it is clear there is no bijectivity, because $(1,3)$ and $(0,4)$ both give $Z = 4$, hence we can not decode $X,Y$ from $Z$.
Computing the entropy $$H(Z) = frac19 log 9 + frac29 log frac92 +frac39 log frac93 + frac29 log frac92 + frac19 log 9 < log(9)$$
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2 Answers
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2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
2
down vote
To construct a proper example, as you found out yourself in a very nice fashion, there is a bijection between $Z$ and $(X,Y)$ (I suppose that random variables are discrete of course).
Let $X'sim textBernoulli(frac 12)$ and $Ysim textBernoulli(frac 12)$. Set $X=2X'$ and take $Z=X+Y$. Then $Z$ is uniformly distributed over $0,1,2,3$ and $X$ and $Y$ are also uniformly distributed over $0,2$ and $0,1$. Therefore it is easy to see that $H(Z)=H(X)+H(Y)$.
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
add a comment |Â
up vote
2
down vote
To construct a proper example, as you found out yourself in a very nice fashion, there is a bijection between $Z$ and $(X,Y)$ (I suppose that random variables are discrete of course).
Let $X'sim textBernoulli(frac 12)$ and $Ysim textBernoulli(frac 12)$. Set $X=2X'$ and take $Z=X+Y$. Then $Z$ is uniformly distributed over $0,1,2,3$ and $X$ and $Y$ are also uniformly distributed over $0,2$ and $0,1$. Therefore it is easy to see that $H(Z)=H(X)+H(Y)$.
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
add a comment |Â
up vote
2
down vote
up vote
2
down vote
To construct a proper example, as you found out yourself in a very nice fashion, there is a bijection between $Z$ and $(X,Y)$ (I suppose that random variables are discrete of course).
Let $X'sim textBernoulli(frac 12)$ and $Ysim textBernoulli(frac 12)$. Set $X=2X'$ and take $Z=X+Y$. Then $Z$ is uniformly distributed over $0,1,2,3$ and $X$ and $Y$ are also uniformly distributed over $0,2$ and $0,1$. Therefore it is easy to see that $H(Z)=H(X)+H(Y)$.
To construct a proper example, as you found out yourself in a very nice fashion, there is a bijection between $Z$ and $(X,Y)$ (I suppose that random variables are discrete of course).
Let $X'sim textBernoulli(frac 12)$ and $Ysim textBernoulli(frac 12)$. Set $X=2X'$ and take $Z=X+Y$. Then $Z$ is uniformly distributed over $0,1,2,3$ and $X$ and $Y$ are also uniformly distributed over $0,2$ and $0,1$. Therefore it is easy to see that $H(Z)=H(X)+H(Y)$.
answered Sep 8 at 20:16
Arash
9,53821537
9,53821537
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
add a comment |Â
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
I like your example +1
â Ahmad Bazzi
Sep 8 at 20:55
add a comment |Â
up vote
1
down vote
In addition to the nice example @Arash gives. Assume the discrete uniform distribution. If $$X sim mathcal U_[a,b](n)$$and$$Y sim mathcal U_[c,d](m)$$
Let's define $$Z = X+Y$$
We know that the entropies of $X$ and $Y$ are $$H(X) = log n$$ and $$H(Y) = log m$$ therefore $$H(X) + H(Y) = log(n) + log(m) = log(nm)$$
This means that if $Z$ has $nm$ outcomes, then we can assert that $H(Z) = H(X) + H(Y)$. Many examples could be generated here to demonstrate the need of being bijective.
One Example out of MANY
Assume $X in lbrace 1,2,3rbrace$ and $Y in lbrace 0,10,20 rbrace$. It is easy to see that $$Z in lbrace 1,2,3,11,12,13,21,22,23 rbrace$$ with $$P(Z = z) = frac19$$
Is $H(Z) = ln(3times 3) = log 9$ ? Of course, because we $X+Y$ here is bijective.
Let's prove it: $$H(Z) = sumlimits_k=1^9 p_k log(frac1p_k) = sumlimits_k=1^9 frac19 log(9) =9frac19 log(9) = log(9)$$
Counter example
Now let's take the same distributions (with different outcomes) but show that $H(Z)$ will not reach $log(9)$ if there is no bijectivity. Let $X in lbrace 0,1,2 rbrace$ and $Y in lbrace 3,4,5 rbrace $. So $$Z in lbrace 3,4,5,6,7 rbrace$$
Therefore, it is clear there is no bijectivity, because $(1,3)$ and $(0,4)$ both give $Z = 4$, hence we can not decode $X,Y$ from $Z$.
Computing the entropy $$H(Z) = frac19 log 9 + frac29 log frac92 +frac39 log frac93 + frac29 log frac92 + frac19 log 9 < log(9)$$
add a comment |Â
up vote
1
down vote
In addition to the nice example @Arash gives. Assume the discrete uniform distribution. If $$X sim mathcal U_[a,b](n)$$and$$Y sim mathcal U_[c,d](m)$$
Let's define $$Z = X+Y$$
We know that the entropies of $X$ and $Y$ are $$H(X) = log n$$ and $$H(Y) = log m$$ therefore $$H(X) + H(Y) = log(n) + log(m) = log(nm)$$
This means that if $Z$ has $nm$ outcomes, then we can assert that $H(Z) = H(X) + H(Y)$. Many examples could be generated here to demonstrate the need of being bijective.
One Example out of MANY
Assume $X in lbrace 1,2,3rbrace$ and $Y in lbrace 0,10,20 rbrace$. It is easy to see that $$Z in lbrace 1,2,3,11,12,13,21,22,23 rbrace$$ with $$P(Z = z) = frac19$$
Is $H(Z) = ln(3times 3) = log 9$ ? Of course, because we $X+Y$ here is bijective.
Let's prove it: $$H(Z) = sumlimits_k=1^9 p_k log(frac1p_k) = sumlimits_k=1^9 frac19 log(9) =9frac19 log(9) = log(9)$$
Counter example
Now let's take the same distributions (with different outcomes) but show that $H(Z)$ will not reach $log(9)$ if there is no bijectivity. Let $X in lbrace 0,1,2 rbrace$ and $Y in lbrace 3,4,5 rbrace $. So $$Z in lbrace 3,4,5,6,7 rbrace$$
Therefore, it is clear there is no bijectivity, because $(1,3)$ and $(0,4)$ both give $Z = 4$, hence we can not decode $X,Y$ from $Z$.
Computing the entropy $$H(Z) = frac19 log 9 + frac29 log frac92 +frac39 log frac93 + frac29 log frac92 + frac19 log 9 < log(9)$$
add a comment |Â
up vote
1
down vote
up vote
1
down vote
In addition to the nice example @Arash gives. Assume the discrete uniform distribution. If $$X sim mathcal U_[a,b](n)$$and$$Y sim mathcal U_[c,d](m)$$
Let's define $$Z = X+Y$$
We know that the entropies of $X$ and $Y$ are $$H(X) = log n$$ and $$H(Y) = log m$$ therefore $$H(X) + H(Y) = log(n) + log(m) = log(nm)$$
This means that if $Z$ has $nm$ outcomes, then we can assert that $H(Z) = H(X) + H(Y)$. Many examples could be generated here to demonstrate the need of being bijective.
One Example out of MANY
Assume $X in lbrace 1,2,3rbrace$ and $Y in lbrace 0,10,20 rbrace$. It is easy to see that $$Z in lbrace 1,2,3,11,12,13,21,22,23 rbrace$$ with $$P(Z = z) = frac19$$
Is $H(Z) = ln(3times 3) = log 9$ ? Of course, because we $X+Y$ here is bijective.
Let's prove it: $$H(Z) = sumlimits_k=1^9 p_k log(frac1p_k) = sumlimits_k=1^9 frac19 log(9) =9frac19 log(9) = log(9)$$
Counter example
Now let's take the same distributions (with different outcomes) but show that $H(Z)$ will not reach $log(9)$ if there is no bijectivity. Let $X in lbrace 0,1,2 rbrace$ and $Y in lbrace 3,4,5 rbrace $. So $$Z in lbrace 3,4,5,6,7 rbrace$$
Therefore, it is clear there is no bijectivity, because $(1,3)$ and $(0,4)$ both give $Z = 4$, hence we can not decode $X,Y$ from $Z$.
Computing the entropy $$H(Z) = frac19 log 9 + frac29 log frac92 +frac39 log frac93 + frac29 log frac92 + frac19 log 9 < log(9)$$
In addition to the nice example @Arash gives. Assume the discrete uniform distribution. If $$X sim mathcal U_[a,b](n)$$and$$Y sim mathcal U_[c,d](m)$$
Let's define $$Z = X+Y$$
We know that the entropies of $X$ and $Y$ are $$H(X) = log n$$ and $$H(Y) = log m$$ therefore $$H(X) + H(Y) = log(n) + log(m) = log(nm)$$
This means that if $Z$ has $nm$ outcomes, then we can assert that $H(Z) = H(X) + H(Y)$. Many examples could be generated here to demonstrate the need of being bijective.
One Example out of MANY
Assume $X in lbrace 1,2,3rbrace$ and $Y in lbrace 0,10,20 rbrace$. It is easy to see that $$Z in lbrace 1,2,3,11,12,13,21,22,23 rbrace$$ with $$P(Z = z) = frac19$$
Is $H(Z) = ln(3times 3) = log 9$ ? Of course, because we $X+Y$ here is bijective.
Let's prove it: $$H(Z) = sumlimits_k=1^9 p_k log(frac1p_k) = sumlimits_k=1^9 frac19 log(9) =9frac19 log(9) = log(9)$$
Counter example
Now let's take the same distributions (with different outcomes) but show that $H(Z)$ will not reach $log(9)$ if there is no bijectivity. Let $X in lbrace 0,1,2 rbrace$ and $Y in lbrace 3,4,5 rbrace $. So $$Z in lbrace 3,4,5,6,7 rbrace$$
Therefore, it is clear there is no bijectivity, because $(1,3)$ and $(0,4)$ both give $Z = 4$, hence we can not decode $X,Y$ from $Z$.
Computing the entropy $$H(Z) = frac19 log 9 + frac29 log frac92 +frac39 log frac93 + frac29 log frac92 + frac19 log 9 < log(9)$$
edited Sep 8 at 21:06
answered Sep 8 at 20:55
Ahmad Bazzi
6,4061624
6,4061624
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