Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distribution testing deals with what information can be deduced about an unknown distribution over $\{1,\ldots,n\}$,#R##N#  where the algorithm is only allod to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allod to obtain samples from the restriction of the original distribution on subsets of $\{1,\ldots,n\}$.#R##N#  #R##N#  In 2015, Canonne, Diakonikolas, Gouleakis and Rubinfeld unified several previous results, and shod that for any property of distributions satisfying a ``decomposability'' criterion, there exists an algorithm (in the basic model) that can distinguish with high probability distributions satisfying the property from distributions that are far from it in variation distance.#R##N#  #R##N#   present here a more efficient yet simpler algorithm for the basic model, as ll as very efficient algorithms for the conditional model, which until now was not investigated under the umbrella of decomposable properties. Additionally,  provide an algorithm for the conditional model that handles a much larger class of properties.#R##N#  #R##N#  Our core mechanism is a way of efficiently producing an interval-partition of $\{1,\ldots,n\}$ that satisfies a ``fine-grain'' quality.  show that with such a partition at hand  can directly move forward with testing individual intervals, instead of first searching for the ``correct'' partition of $\{1,\ldots,n\}$.
['Eldar Fischer', 'Oded Lachish', 'Yadu Vasudev']
I am writing a research paper on a idea Improving and extending the testing of distributions for shape-restricted properties
[{"from": "human", "value": "I am writing a research paper on a idea Improving and extending the testing of distributions for shape-restricted properties"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Distribution testing deals with what information can be deduced about an unknown distribution over $\\{1,\\ldots,n\\}$,#R##N#  where the algorithm is only allod to obtain a relatively small number of independent samples from the distribution. In the extended conditional sampling model, the algorithm is also allod to obtain samples from the restriction of the original distribution on subsets of $\\{1,\\ldots,n\\}$.#R##N#  #R##N#  In 2015, Canonne, Diakonikolas, Gouleakis and Rubinfeld unified several previous results, and shod that for any property of distributions satisfying a ``decomposability'' criterion, there exists an algorithm (in the basic model) that can distinguish with high probability distributions satisfying the property from distributions that are far from it in variation distance.#R##N#  #R##N#   present here a more efficient yet simpler algorithm for the basic model, as ll as very efficient algorithms for the conditional model, which until now was not investigated under the umbrella of decomposable properties. Additionally,  provide an algorithm for the conditional model that handles a much larger class of properties.#R##N#  #R##N#  Our core mechanism is a way of efficiently producing an interval-partition of $\\{1,\\ldots,n\\}$ that satisfies a ``fine-grain'' quality.  show that with such a partition at hand  can directly move forward with testing individual intervals, instead of first searching for the ``correct'' partition of $\\{1,\\ldots,n\\}$."}]