Perhaps you can try taking the limit of this function as x -> 0.

The function cannot be directly evaluated at x=0, but it does approach a value of 1 as x tends to 0. This is similar to the case of f(x)=sin(x)/x.



As such, you make take the limit to evaluate f(0) as Krevaki has mentioned.

This post has been edited by v1n0d: May 2 2016, 02:11 PM

How should I find the moment of inertia about z-axis of hemispherical with these properties?
Distribution of mass on hemispherical shell z = √(R² - x² - y²) is σ = (σ₀/R²)(x² + y²).

I tried using ∫∫ σ • 1/(n·k) dS to find an expression for total mass, got very stuck and tangled in the integration process halp, and am still trying to figure out how to find the moment of inertia the proper way (without cheating with formulas given in books).

is it possible to calculate double integral in casio fx-570es plus ? how to do that ?

Hi, can someone help me with part © of this question? Thank you very much. Below is the full question.

In this problem, we model a queue using a Markov chain. (the queue might represent,
for example, customers waiting to buy something at a shop, a web server attempting to
respond to page requests, or students waiting during office hours.)

Suppose that a certain queue may contain 0, 1, or 2 items.
It is not possible for it to contain 3 or more items. At each time step, one of two things can happen:
(i)with probability 1/3, one item is removed from the queue, if there is an item to remove
(and otherwise nothing happens),
or (ii) with probability 2/3, one item is added to the queue, if there is enough space (i.e. if
there are not already 2 items in the queue) (and otherwise nothing happens).

(a) Formulate a finite Markov chain that describes this system.

(b)If the queue starts off empty, after 4 time steps what is the probability that it is again
empty?


©In the long term, what is the average rate at which items are removed from the queue?

Hi guy , I trying to proof that

Limit of [1/x^2] as x->[sqrt(3)] = 1/3

using the epsilon delta definition of limit.
I cant even express delta in form of epsilon .-.

Watch this, it'll give you an idea on how to proceed:

Hi,

I am trying to fit a bivariate discrete distribution to a set of data via ML, say for example my table of observations for X is 0, 1, 2, and >=3, and the same as for Y.

We let f(x_i,y_i) = Pr[X_i=x_i, Y_i=y_i] be the pf for the discrete bivariate distribution. If say I'm fitting a bivariate negative binomial distribution to the data, then the support for x, y will be \in \mathbb{N}.

I want to write down the likelihood equation L(param), there is no problem for x,y = 0, 1, 2, as I write out \prod f(i, j), for i,j = 0,1,2.

However, as for the case {X>=3, Y=i}, {Y>=3, X=i} for i=0,1,2; is it valid to write the probability function as Pr[X>=3 , Y=0], Pr[X>=3, Y=1] (which just means 1 - Pr[X=0,Y=0] - Pr[X=1, Y=0] - Pr[X=2, Y=0] in the latter case) and so on for the likelihood equation? It seems weird because it has a cumulative part for X and a probability mass part for Y

This post has been edited by Leptokurtosis: Jul 25 2017, 02:45 PM