Movies Section: On the list, What is your favorite movies of 1998 each film distribution?

For example of my favorites.

Disney: A Bug’s Life

HM: Mulan, The Waterboy, Armageddon, Shakespeare in Love, The Parents Trap, The Faculty, Rounders, Enemy of the State, I Got the Hook Up, Beloved, Ride, Down in the Delta, Life Is Beautiful, Simon Birch, He Got Game; and Six Days, Seven Nights

Fox: Dr. Dolittle

HM: There’s Something About Mary, Bulworth, The X-Flies, The Thin Red Line, Ever After, and How Stella Got Her Groove Back

Lionsgate: Belly

HM: Permanent Midnight, Cube, Star Kid, Buffalo ’66, Suicide Kings, Caught Up, and Pi

MGM: The Man in the Iron Mask

HM: Ronin, Species II, Disturbing Behavior, and Deceiver

Paramount: The Truman Show

HM: The Rugrats Movie, Deep Impact, A Simple Plan, Snake Eyes, Sliding Doors, Hard Rain, and Star Trek: Insurrection

Sony: The Mask of Zorro

HM: Les Misérables, Godzilla, Spice World, Can’t Hardly Wait, I Still Know What You Did Last Summer, Stepmom, Wild Things, Urban Legend, The Replacement Killers, and Madeline

Universal: Saving Private Ryan

HM: The Big Lebowski, Antz, The Prince of Egypt, Half-Baked, Patch Adams, Babe: Pig in the City, BASKetball, Out of Sight, Small Soldiers, What Dreams May Come, Bride of Chucky, Mercury Rising, Primary Colors, Meet Joe Black, Paulie, The Last Days of Disco, Elizabeth, and Black Dog

Updated 34 mins ago:

Warner Bros.: Rush Hour

HM: Lethal Weapon 4, You’ve Got Mail, The Negotiator, Mr. Nice Guy, Quest for Camelot, Pleasantville, Why Do Fools Fall in Love, Blade, The Wedding Singer, City of Angels, The Players Club, Practical Magic, Lost in Space, Sphere, Home Fries, Almost Heroes, and American History X

If you don’t know any of these movies or haven’t seen or haven’t ever heard of, you can skip it or put none.

How do I mathematically find this distribution?

I roll 10d6. I can then reroll any ones or twos. Then, I can reroll any ones. What is the distribution of the number of sixes I will have?

software distribution – how to distribute an application that needs an api key to work

I am trying to distribute a project that makes requests from an external API. My only problem is that I need to use a secret key to make an API request. My program is entirely on the client side, so how can I distribute it without needing a server in the middle to handle the program requests to the specific API.

Finding a function of $Pois(lambda_1)$ and $Pois(lambda_2)$ such that the distribution depends on $frac{lambda_1}{lambda_1+lambda_2}$ only.

Suppose that $Xsim Pois(lambda_1)$ and $Ysim Pois(lambda_2)$ are two independent Poisson random variables. Can we find a function $f(X,Y)$ of $X$ and $Y$, where $f(X,Y)$ does not involve $lambda_1$ and $lambda_2$, such that the distribution of $Zequiv f(X,Y)$ depends on the parameter $frac{lambda_1}{lambda_1+lambda_2}$ only?

I know there is a result that $X|X+Y=n$ follows a binomial distribution with parameters $n$ and $frac{lambda_1}{lambda_1+lambda_2}$. But can we find a statistic $Z= f(X,Y)$ without conditioning such that $Z$ depends on the parameter $frac{lambda_1}{lambda_1+lambda_2}$ only?

I tried some naive functions such as $f(X,Y)=frac{X}{X+Y+1}$ and $f(X,Y)=frac{X}{X^2+Y^2+1}$, but it turns out that those functions do not work.

co.combinatorics – Average number of edges of an induced graph by using the edge-based node selection technique on a graph with arbitrary degree distribution

Let $G(U,V,E)$ be a simple, undirected, bipartite graph and $U={u_1,u_2,{cdots},u_n}$ and $V={ v_{1},v_{2},cdots,v_{n}}$. Let $d_k^l$ be the number of vertex with degree $k$ in $l$, where $l in L={U,V}$ and $k in X={1,2,cdots,n}$. Note that $ M = |E|= frac{1}{2}sum_{i in X }sum_{j in L}d_i^j$ is the total number of edges of $G$.

Now, randomly select $m$ edges from $E$ without replacement and call a vertex “covered” if it is one of the ends of the randomly selected edges. Then, induce a graph with the covered vertex subset ($S$), i.e, a graph $G_{S}$ with vertex set $S$ and all the edges connecting pairs of vertices in that subset in $G$.

The question is: is there any way to derive the expected value of the number of edges of $G_{S}$ ($E{(E_{s})}$) based on the probability of edge selection $p_{e} = frac{m}{M}$ and the degree distribution of $G$?

For the case of the uniform degree distribution, the results in 1 or 2 can be applied, but I would like to see if there is a way to calculate it for an arbitrary degree distribution.

Fitting data to left skewed gamma distribution

How can I fit the following two set of data to a left skewed gamma function, which I what I think should fit the data best?:

data 1 is here: https://pastebin.com/X2HTjTP7
data 2 is here: https://pastebin.com/8Rh4BHDT

Is there any other suggestion of what would be the best distribution or equation to fit the data?

A picture of how data 1 looks is here:

data 1

A picture of how data 2 looks is here:

data 2

Thank you in advanced,

Need help with a specific distribution algorithm

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pr.probability – expectation of the exponential of the inverse of variable with Marchenko–Pastur distribution

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architectural models – Batch processing: Solutions for the distribution of workloads

I am working on a product which is a multi-tenant cloud solution. When it comes to repeatable batch processing, we have a defined pattern.

  1. We configure a job for it to wake up and start executing its logic at regular intervals. More precisely, at each execution, it browses all the tenants, one by one, reads the information relating to a respective tenant (stored in a database) and executes the business logic
  2. We have more than one instance of a particular job running (on different nodes / servers) to ensure fault tolerance. Thus, an instance of a job acquires a lock on a database table row that is statically mapped to the job. By "acquire", I mean that the work marks a column in this row as claimed. This ensures that only one of multiple instances of the same job acquires the lock and processes the data. This way we have no duplicate treatment of a tenant.

In recent times we have seen the need to re-architect this to deal with larger volumes and general scalability issues.

We want to follow the path of multiple work instances working on mutually divided workloads so that we use our resources better and our throughput increases.

Are there any known models / technologies for doing this?

stochastic processes – calculate the regime permutation correlation matrix without assumption about the distribution

There is an abundant literature on the dynamic Markov regime change correlation matrix. But they seem to assume a certain type of distribution and use MLE / EM. For example, a kind of multivariate GARCH plus a normal multivariate distribution.

Is it necessary? Since the empirical correlation matrix is ​​free from distributional assumptions, is there any way to calculate the regime change correlation matrix without it?