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Probability distribution functionsPowerPoint Presentation

Probability distribution functions

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Probability distribution functions

- Normal distribution
- Lognormal distribution
- Mean, median and mode
- Tails
- Extreme value distributions

Normal (Gaussian) distribution

- Probability density function (PDF)
- What does figure tell about the cumulative distribution function (CDF)?

More on the normal distribution

- Normal distribution is denoted , with the square giving the variance.
- If X is normal,Y=aX+bis also normal. What would be the mean and standard deviation of Y?
- Similarly, if X and Y are normal variables, any linear combination, aX+bY is also normal.
- Can often use any function of a normal random variables by using a linear Taylor expansion.
- Example: X=N(10,0.52) and Y=X2. Then YN(100,102)

Estimating mean and standard deviation

- Given a sample from a normally distributed variable, the sample mean is the best linear unbiased estimator (BLUE) of the true mean.
- For the variance the equation gives the best unbiased estimator, but the square root is not an unbiased estimate of the standard deviation
- For example, for a sample of 5 from a standard normal distribution, the standard deviation will be estimated on average as 0.94 (with standard deviation of 0.34)

Lognormal distribution

- If ln(X) has normal distribution X has lognormal distribution. That is, if X is normally distributed exp(X) is lognormally distributed.
- Notation:
- Mean and variance

Mean, mode and median

- Mode (highest point) =
- Median (50% of samples)
- Figure for =0.

Light and heavy tails

- Normal distribution has light tail; 4.5 sigma is equivalent to 3.4e-6 failure or defect probability.
- Lognormal can have heavy tail

Fitting distribution to data

- Usually fit CDF to minimize maximum distance (Kolmogorov-Smirnoff test)
- Generated 20 points from N(3,12).
- Normal fit N(3.48,0.932)
- Lognormal lnN(1.24,0.26)
Almost same mean and

standard deviation.

Extreme value distributions

- No matter what distribution you sample from, the mean of the sample tends to be normally distributed as sample size increases (what mean and standard deviation?)
- Similarly, distributions of the minimum (or maximum) of samples belong to other distributions.
- Even though there are infinite number of distributions, there are only three extreme value distribution.
- Type I (Gumbel) derived from normal.
- Type II (Frechet) e.g. maximum daily rainfall
- Type III (Weibull) weakest link failure

Maximum of normal samples

With normal distribution, maximum of sample is more narrowly distributed than original distribution.

Max of 10 standard normal samples. 1.54 mean, 0.59 standard deviation

Max of 100 standard normal samples. 2.50 mean, 0.43 standard deviation

Gumbel distribution

- .
- Mean, median, mode and variance

Weibull distribution

- Probability distribution
- Its log has Gumbel dist.

- Used to describe distribution of strength or fatigue life in brittle materials.
- If it describes time to failure, then
- k<1 indicates that failure rate decreases with time,
- k=1 indicates constant rate,
- k>1 indicates increasing rate.

- Can add 3rd parameter by replacing x by x-c.

Exercises

- Find how many samples of normally distributed numbers you need in order to estimate the mean and standard deviation with an error that will be less than 10% of the true standard deviation most of the time.
- Both the lognormal and Weibull distributions are used to model strength. Find how closely you can approximate data generated from a standard lognormal distribution by fitting it with Weibull.
- Take the introduction and preamble of the US Declaration of Independence, and fit the distribution of word lengths using the K-S criterion. What distribution fits best? Compare the graphs of the CDFs. Compare to a more contemporary text.

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