Episode #125 of the Stack Overflow podcast is here. We talk Tilde Club and mechanical keyboards. Listen now

Search Results

Results tagged with Search options answers only user 2958
9 results

Probability density function (PDF) of a continuous random variable gives the relative probability for each of its possible values. Use this tag for discrete probability mass functions (PMFs) too.

$P( Y \leq y)$ $= P( Y \leq y|X=1)P(X=1) + P( Y \leq y|X=0)P(X=0)$ $=P( Y \leq y|X=1)p + P( Y \leq y|X=0)(1-p)$ $=pP( W\leq y) + (1-p)P( Z \leq y)$ So $F_Y(y)=pF_W(y)+(1-p)F_Z(y)$ and thus $f_Y(y … answered Oct 27 '13 by Henry An equivalent result is well known in survival analysis: the expected lifetime is $$\int_{t=0}^\infty S(t) \; dt$$ where the survival function is$S(t) = \Pr(T \gt t)$measured from birth at$t=0$. ( … answered Nov 15 '11 by Henry From the R package MASS, of the$506$total observations in Boston,$369$have a value for tax below 470 and$137$have a value for tax above 665. In fact 666 is by far the most common value in the d … answered Feb 12 '14 by Henry You can extend your non-parametric method if your original sample is large enough. Suppose you wanted to have a 95% probability of not rejecting the null hypothesis that your new observation comes … answered Sep 17 '13 by Henry You should not have$X_1$in the marginal distribution for$X_2$I would expect you to get$P(X_2 \le x_2) = x_2 (1-\log(x_2))$and so the derivative gives a marginal density of$-\log(x_2)$. This … answered Feb 25 '11 by Henry Here is one made specially for you. Note that the density of a distribution symmetric about$0$is the same for positive and negative values. density cumprob -3.5 0.0008726827 0.00023 … answered Dec 17 '12 by Henry I would call$f_t$the instantaneous rate of the process at time$t$, or perhaps the hazard function So, for example, you can find the expected number of arrivals between time$a$and time$b$, whi … answered Oct 17 '18 by Henry A simple example would be if you bought a light-bulb with a lifetime which was exponentially distributed with a mean of 1000 days. With a 95% credible region: would you tend to see it as likely to l … answered Mar 14 '12 by Henry In your link, you have the cumulative distribution function for the logistic distribution as $$\frac{1}{1+e^{-\frac{x-\mu}{s}}}$$ while in your question you have$\$\dfrac{\exp(w^TX)}{(1+\exp(w^TX))} …
answered Oct 1 by Henry