A Multiplicative version of McDiarmid’s Inequality like the one of Chernoff-Hoeffding Bounds












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McDiarmid's Inequality basically says the following:

Let $X_1, X_2, X_3, ldots, X_n$ denote independent random variables and $f$ is a function of $n$ real arguments. If changing the value of the $i$-th variable changes the value of $f$ by at most $c_i$, then
$$Pr(f > E[f]+t), Pr(f < E[f] -t) leq expleft(frac{-2t^2}{sum_i c_i^2}right)$$
Is there a known multiplicative version of this inequality, i.e. we have $Pr(f > (1+epsilon)E[f])$ on the left hand side.

The Chernoff-Hoeffding Bounds actually have the corresponding two versions:
$$Pr[X > E[X] +t] leq exp(-2t^2/n)$$
$$Pr[X > (1+epsilon)E[X]] < exp(-epsilon^2E[X]/3).$$



I am wondering anyone has thought about it for McDiarmid's Inequality. Is it possible to derive the same multiplicative version just by going though the proofs of both of them?










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    2












    $begingroup$


    McDiarmid's Inequality basically says the following:

    Let $X_1, X_2, X_3, ldots, X_n$ denote independent random variables and $f$ is a function of $n$ real arguments. If changing the value of the $i$-th variable changes the value of $f$ by at most $c_i$, then
    $$Pr(f > E[f]+t), Pr(f < E[f] -t) leq expleft(frac{-2t^2}{sum_i c_i^2}right)$$
    Is there a known multiplicative version of this inequality, i.e. we have $Pr(f > (1+epsilon)E[f])$ on the left hand side.

    The Chernoff-Hoeffding Bounds actually have the corresponding two versions:
    $$Pr[X > E[X] +t] leq exp(-2t^2/n)$$
    $$Pr[X > (1+epsilon)E[X]] < exp(-epsilon^2E[X]/3).$$



    I am wondering anyone has thought about it for McDiarmid's Inequality. Is it possible to derive the same multiplicative version just by going though the proofs of both of them?










    share|cite|improve this question











    $endgroup$















      2












      2








      2





      $begingroup$


      McDiarmid's Inequality basically says the following:

      Let $X_1, X_2, X_3, ldots, X_n$ denote independent random variables and $f$ is a function of $n$ real arguments. If changing the value of the $i$-th variable changes the value of $f$ by at most $c_i$, then
      $$Pr(f > E[f]+t), Pr(f < E[f] -t) leq expleft(frac{-2t^2}{sum_i c_i^2}right)$$
      Is there a known multiplicative version of this inequality, i.e. we have $Pr(f > (1+epsilon)E[f])$ on the left hand side.

      The Chernoff-Hoeffding Bounds actually have the corresponding two versions:
      $$Pr[X > E[X] +t] leq exp(-2t^2/n)$$
      $$Pr[X > (1+epsilon)E[X]] < exp(-epsilon^2E[X]/3).$$



      I am wondering anyone has thought about it for McDiarmid's Inequality. Is it possible to derive the same multiplicative version just by going though the proofs of both of them?










      share|cite|improve this question











      $endgroup$




      McDiarmid's Inequality basically says the following:

      Let $X_1, X_2, X_3, ldots, X_n$ denote independent random variables and $f$ is a function of $n$ real arguments. If changing the value of the $i$-th variable changes the value of $f$ by at most $c_i$, then
      $$Pr(f > E[f]+t), Pr(f < E[f] -t) leq expleft(frac{-2t^2}{sum_i c_i^2}right)$$
      Is there a known multiplicative version of this inequality, i.e. we have $Pr(f > (1+epsilon)E[f])$ on the left hand side.

      The Chernoff-Hoeffding Bounds actually have the corresponding two versions:
      $$Pr[X > E[X] +t] leq exp(-2t^2/n)$$
      $$Pr[X > (1+epsilon)E[X]] < exp(-epsilon^2E[X]/3).$$



      I am wondering anyone has thought about it for McDiarmid's Inequality. Is it possible to derive the same multiplicative version just by going though the proofs of both of them?







      probability inequality






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      share|cite|improve this question













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      edited Jan 20 at 12:04









      Lee David Chung Lin

      4,37031241




      4,37031241










      asked Dec 4 '12 at 21:26









      Steven WuSteven Wu

      111




      111






















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