Given a one-dimensional probability density function, describe how you would find the 95% confidence interval. Explain...
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Given a one-dimensional probability density function, describe how you would find the 95% confidence interval. Explain what inverse variance weighting is and why it is commonly useful. Given a data set {i:0<i<N} of N values, where each value x; is independent and drawn from a normal (Gaussian) distribution. The values have different (known) standard deviations, given by o₁. Use Bayes' theorem (derived in question 1(a)) to prove that the most probable value o for the mean of the set is the weighted average of the data. Assume a flat prior for the mean . Given a one-dimensional probability density function, describe how you would find the 95% confidence interval. Explain what inverse variance weighting is and why it is commonly useful. Given a data set {i:0<i<N} of N values, where each value x; is independent and drawn from a normal (Gaussian) distribution. The values have different (known) standard deviations, given by o₁. Use Bayes' theorem (derived in question 1(a)) to prove that the most probable value o for the mean of the set is the weighted average of the data. Assume a flat prior for the mean .
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In statistics inversevariance weighting is a method of aggregating two or more random variables to minimize the variance of the weighted average Each random variable is weighted in inverse proportion ... View the full answer
Related Book For
Systems analysis and design
ISBN: 978-0136089162
8th Edition
Authors: kenneth e. kendall, julie e. kendall
Posted Date:
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