distributions.md (22036B)
1 ## Distributions 2 3 4 ### jStat.beta( alpha, beta ) 5 6 #### jStat.beta.pdf( x, alpha, beta ) 7 8 Returns the value of `x` in the Beta distribution with parameters `alpha` and `beta`. 9 10 #### jStat.beta.cdf( x, alpha, beta ) 11 12 Returns the value of `x` in the cdf for the Beta distribution with parameters `alpha` and `beta`. 13 14 #### jStat.beta.inv( p, alpha, beta ) 15 16 Returns the value of `p` in the inverse of the cdf for the Beta distribution with parameters `alpha` and `beta`. 17 18 #### jStat.beta.mean( alpha, beta ) 19 20 Returns the mean of the Beta distribution with parameters `alpha` and `beta`. 21 22 #### jStat.beta.median( alpha, beta ) 23 24 Returns the median of the Beta distribution with parameters `alpha` and `beta`. 25 26 #### jStat.beta.mode( alpha, beta ) 27 28 Returns the mode of the Beta distribution with parameters `alpha` and `beta`. 29 30 #### jStat.beta.sample( alpha, beta ) 31 32 Returns a random number whose distribution is the Beta distribution with parameters `alpha` and `beta`. 33 34 #### jStat.beta.variance( alpha, beta ) 35 36 Returns the variance of the Beta distribution with parameters `alpha` and `beta`. 37 38 ### jStat.centralF( df1, df2 ) 39 40 The F Distrbution is used frequently in analyses of variance. The distribution is parameterized by two degrees of freedom (`df1` and `df2`). It is defined continuously on x in [0, infinity). 41 42 In all cases, `df1` is the "numerator degrees of freedom" and `df2` is the "denominator degrees of freedom", which parameterize the distribtuion. 43 44 #### jStat.centralF.pdf( x, df1, df2 ) 45 46 Given `x` in the range [0, infinity), returns the probability density of the (central) F distribution at `x`. 47 48 This function corresponds to the `df(x, df1, df2)` function in R. 49 50 #### jStat.centralF.cdf( x, df1, df2 ) 51 52 Given x in the range [0, infinity), returns the cumulative probability density of the central F distribution. That is, `jStat.centralF.cdf(2.5, 10, 20)` will return the probability that a number randomly selected from the central F distribution with `df1 = 10` and `df2 = 20` will be less than 2.5. 53 54 This function corresponds to the `pf(q, df1, df2)` function in R. 55 56 #### jStat.centralF.inv( p, df1, df2 ) 57 58 Given `p` in [0, 1), returns the value of x for which the cumulative probability density of the central F distribution is p. That is, `jStat.centralF.inv(p, df1, df2) = x` if and only if `jStat.centralF.inv(x, df1, df2) = p`. 59 60 This function corresponds to the `qf(p, df1, df2)` function in R. 61 62 #### jStat.centralF.mean( df1, df2 ) 63 64 Returns the mean of the (Central) F distribution. 65 66 #### jStat.centralF.mode( df1, df2 ) 67 68 Returns the mode of the (Central) F distribution. 69 70 #### jStat.centralF.sample( df1, df2 ) 71 72 Returns a random number whose distribution is the (Central) F distribution. 73 74 This function corresponds to the `rf(n, df1, df2)` function in R. 75 76 #### jStat.centralF.variance( df1, df2 ) 77 78 Returns the variance of the (Central) F distribution. 79 80 ### jStat.cauchy( local, scale ) 81 82 #### jStat.cauchy.pdf( x, local, scale ) 83 84 Returns the value of `x` in the pdf of the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 85 86 #### jStat.cauchy.cdf( x, local, scale ) 87 88 Returns the value of `x` in the cdf of the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 89 90 #### jStat.cauchy.inv( p, local, scale ) 91 92 Returns the value of `p` in the inverse of the cdf for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 93 94 #### jStat.cauchy.median( local, scale ) 95 96 Returns the value of the median for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 97 98 #### jStat.cauchy.mode( local, scale ) 99 100 Returns the value of the mode for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 101 102 #### jStat.cauchy.sample( local, scale ) 103 104 Returns a random number whose distribution is the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 105 106 #### jStat.cauchy.variance( local, scale ) 107 108 Returns the value of the variance for the Cauchy distribution with a location (median) of `local` and scale factor of `scale`. 109 110 ### jStat.chisquare( dof ) 111 112 #### jStat.chisquare.pdf( x, dof ) 113 114 Returns the value of `x` in the pdf of the Chi Square distribution with `dof` degrees of freedom. 115 116 #### jStat.chisquare.cdf( x, dof ) 117 118 Returns the value of `x` in the cdf of the Chi Square distribution with `dof` degrees of freedom. 119 120 #### jStat.chisquare.inv( p, dof ) 121 122 Returns the value of `x` in the inverse of the cdf for the Chi Square distribution with `dof` degrees of freedom. 123 124 #### jStat.chisquare.mean( dof ) 125 126 Returns the value of the mean for the Chi Square distribution with `dof` degrees of freedom. 127 128 #### jStat.chisquare.median( dof ) 129 130 Returns the value of the median for the Chi Square distribution with `dof` degrees of freedom. 131 132 #### jStat.chisquare.mode( dof ) 133 134 Returns the value of the mode for the Chi Square distribution with `dof` degrees of freedom. 135 136 #### jStat.chisquare.sample( dof ) 137 138 Returns a random number whose distribution is the Chi Square distribution with `dof` degrees of freedom. 139 140 #### jStat.chisquare.variance( dof ) 141 142 Returns the value of the variance for the Chi Square distribution with `dof` degrees of freedom. 143 144 145 ### jStat.exponential( rate ) 146 147 #### jStat.exponential.pdf( x, rate ) 148 149 Returns the value of `x` in the pdf of the Exponential distribution with the parameter `rate` (lambda). 150 151 #### jStat.exponential.cdf( x, rate ) 152 153 Returns the value of `x` in the cdf of the Exponential distribution with the parameter `rate` (lambda). 154 155 #### jStat.exponential.inv( p, rate ) 156 157 Returns the value of `p` in the inverse of the cdf for the Exponential distribution with the parameter `rate` (lambda). 158 159 #### jStat.exponential.mean( rate ) 160 161 Returns the value of the mean for the Exponential distribution with the parameter `rate` (lambda). 162 163 #### jStat.exponential.median( rate ) 164 165 Returns the value of the median for the Exponential distribution with the parameter `rate` (lambda) 166 167 #### jStat.exponential.mode( rate ) 168 169 Returns the value of the mode for the Exponential distribution with the parameter `rate` (lambda). 170 171 #### jStat.exponential.sample( rate ) 172 173 Returns a random number whose distribution is the Exponential distribution with the parameter `rate` (lambda). 174 175 #### jStat.exponential.variance( rate ) 176 177 Returns the value of the variance for the Exponential distribution with the parameter `rate` (lambda). 178 179 ### jStat.gamma( shape, scale ) 180 181 #### jStat.gamma.pdf( x, shape, scale ) 182 183 Returns the value of `x` in the pdf of the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 184 185 #### jStat.gamma.cdf( x, shape, scale ) 186 187 Returns the value of `x` in the cdf of the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 188 189 This function is checked against R's `pgamma` function. 190 191 #### jStat.gamma.inv( p, shape, scale ) 192 193 Returns the value of `p` in the inverse of the cdf for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 194 195 This function is checked against R's `qgamma` function. 196 197 #### jStat.gamma.mean( shape, scale ) 198 199 Returns the value of the mean for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 200 201 #### jStat.gamma.mode( shape, scale ) 202 203 Returns the value of the mode for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 204 205 #### jStat.gamma.sample( shape, scale ) 206 207 Returns a random number whose distribution is the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 208 209 #### jStat.gamma.variance( shape, scale ) 210 211 Returns the value of the variance for the Gamma distribution with the parameters `shape` (k) and `scale` (theta). Notice that if using the alpha beta convention, `scale = 1/beta`. 212 213 ### jStat.invgamma( shape, scale ) 214 215 #### jStat.invgamma.pdf( x, shape, scale ) 216 217 Returns the value of `x` in the pdf of the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 218 219 #### jStat.invgamma.cdf( x, shape, scale ) 220 221 Returns the value of `x` in the cdf of the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 222 223 #### jStat.invgamma.inv( p, shape, scale ) 224 225 Returns the value of `p` in the inverse of the cdf for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 226 227 #### jStat.invgamma.mean( shape, scale ) 228 229 Returns the value of the mean for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 230 231 #### jStat.invgamma.mode( shape, scale ) 232 233 Returns the value of the mode for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 234 235 #### jStat.invgamma.sample( shape, scale ) 236 237 Returns a random number whose distribution is the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 238 239 #### jStat.invgamma.variance( shape, scale ) 240 241 Returns the value of the variance for the Inverse-Gamma distribution with parametres `shape` (alpha) and `scale` (beta). 242 243 ### jStat.kumaraswamy( alpha, beta ) 244 245 #### jStat.kumaraswamy.pdf( x, a, b ) 246 247 Returns the value of `x` in the pdf of the Kumaraswamy distribution with parameters `a` and `b`. 248 249 #### jStat.kumaraswamy.cdf( x, alpha, beta ) 250 251 Returns the value of `x` in the cdf of the Kumaraswamy distribution with parameters `alpha` and `beta`. 252 253 #### jStat.kumaraswamy.inv( p, alpha, beta ) 254 255 Returns the value of `p` in the inverse of the pdf for the Kumaraswamy distribution with parametres `alpha` and `beta`. 256 257 This function corresponds to `qkumar(p, alpha, beta)` in R's VGAM package. 258 259 #### jStat.kumaraswamy.mean( alpha, beta ) 260 261 Returns the value of the mean of the Kumaraswamy distribution with parameters `alpha` and `beta`. 262 263 #### jStat.kumaraswamy.median( alpha, beta ) 264 265 Returns the value of the median of the Kumaraswamy distribution with parameters `alpha` and `beta`. 266 267 #### jStat.kumaraswamy.mode( alpha, beta ) 268 269 Returns the value of the mode of the Kumaraswamy distribution with parameters `alpha` and `beta`. 270 271 #### jStat.kumaraswamy.variance( alpha, beta ) 272 273 Returns the value of the variance of the Kumaraswamy distribution with parameters `alpha` and `beta`. 274 275 ### jStat.lognormal( mu, sigma ) 276 277 #### jStat.lognormal.pdf( x, mu, sigma ) 278 279 Returns the value of `x` in the pdf of the Log-normal distribution with paramters `mu` (mean) and `sigma` (standard deviation). 280 281 #### jStat.lognormal.cdf( x, mu, sigma ) 282 283 Returns the value of `x` in the cdf of the Log-normal distribution with paramters `mu` (mean) and `sigma` (standard deviation). 284 285 #### jStat.lognormal.inv( p, mu, sigma ) 286 287 Returns the value of `x` in the inverse of the cdf for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 288 289 #### jStat.lognormal.mean( mu, sigma ) 290 291 Returns the value of the mean for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 292 293 #### jStat.lognormal.median( mu, sigma ) 294 295 Returns the value of the median for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 296 297 #### jStat.lognormal.mode( mu, sigma ) 298 299 Returns the value of the mode for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 300 301 #### jStat.lognormal.sample( mu, sigma ) 302 303 Returns a random number whose distribution is the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 304 305 #### jStat.lognormal.variance( mu, sigma ) 306 307 Returns the value of the variance for the Log-normal distribution with paramters `mu` (mean of the Normal distribution) and `sigma` (standard deviation of the Normal distribution). 308 309 ### jStat.normal( mean, std ) 310 311 #### jStat.normal.pdf( x, mean, std ) 312 313 Returns the value of `x` in the pdf of the Normal distribution with parameters `mean` and `std` (standard deviation). 314 315 #### jStat.normal.cdf( x, mean, std ) 316 317 Returns the value of `x` in the cdf of the Normal distribution with parameters `mean` and `std` (standard deviation). 318 319 #### jStat.normal.inv( p, mean, std ) 320 321 Returns the value of `p` in the inverse cdf for the Normal distribution with parameters `mean` and `std` (standard deviation). 322 323 #### jStat.normal.mean( mean, std ) 324 325 Returns the value of the mean for the Normal distribution with parameters `mean` and `std` (standard deviation). 326 327 #### jStat.normal.median( mean, std ) 328 329 Returns the value of the median for the Normal distribution with parameters `mean` and `std` (standard deviation). 330 331 #### jStat.normal.mode( mean, std ) 332 333 Returns the value of the mode for the Normal distribution with parameters `mean` and `std` (standard deviation). 334 335 #### jStat.normal.sample( mean, std ) 336 337 Returns a random number whose distribution is the Normal distribution with parameters `mean` and `std` (standard deviation). 338 339 #### jStat.normal.variance( mean, std ) 340 341 Returns the value of the variance for the Normal distribution with parameters `mean` and `std` (standard deviation). 342 343 ### jStat.pareto( scale, shape ) 344 345 #### jStat.pareto.pdf( x, scale, shape ) 346 347 Returns the value of `x` in the pdf of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 348 349 #### jStat.pareto.inv( p, scale, shape ) 350 351 Returns the inverse of the Pareto distribution with probability `p`, `scale`, `shape`. 352 353 This coresponds to `qpareto(p, scale, shape)` in R's VGAM package, and generally corresponds to the `q`<dist> function pattern in R. 354 355 #### jStat.pareto.cdf( x, scale, shape ) 356 357 Returns the value of `x` in the cdf of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 358 359 #### jStat.pareto.mean( scale, shape ) 360 361 Returns the value of the mean of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 362 363 #### jStat.pareto.median( scale, shape ) 364 365 Returns the value of the median of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 366 367 #### jStat.pareto.mode( scale, shape ) 368 369 Returns the value of the mode of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 370 371 #### jStat.pareto.variance( scale, shape ) 372 373 Returns the value of the variance of the Pareto distribution with parameters `scale` (x<sub>m</sub>) and `shape` (alpha). 374 375 ### jStat.studentt( dof ) 376 377 #### jStat.studentt.pdf( x, dof ) 378 379 Returns the value of `x` in the pdf of the Student's T distribution with `dof` degrees of freedom. 380 381 #### jStat.studentt.cdf( x, dof ) 382 383 Returns the value of `x` in the cdf of the Student's T distribution with `dof` degrees of freedom. 384 385 #### jStat.studentt.inv( p, dof ) 386 387 Returns the value of `p` in the inverse of the cdf for the Student's T distribution with `dof` degrees of freedom. 388 389 #### jStat.studentt.mean( dof ) 390 391 Returns the value of the mean of the Student's T distribution with `dof` degrees of freedom. 392 393 #### jStat.studentt.median( dof ) 394 395 Returns the value of the median of the Student's T distribution with `dof` degrees of freedom. 396 397 #### jStat.studentt.mode( dof ) 398 399 Returns the value of the mode of the Student's T distribution with `dof` degrees of freedom. 400 401 #### jStat.studentt.sample( dof ) 402 403 Returns a random number whose distribution is the Student's T distribution with `dof` degrees of freedom. 404 405 #### jStat.studentt.variance( dof ) 406 407 Returns the value of the variance for the Student's T distribution with `dof` degrees of freedom. 408 409 ### jStat.tukey( nmeans, dof ) 410 411 #### jStat.tukey.cdf( q, nmeans, dof ) 412 413 Returns the value of q in the cdf of the Studentized range distribution with `nmeans` number of groups nmeans and `dof` degrees of freedom. 414 415 #### jStat.tukey.inv( p, nmeans, dof ) 416 417 Returns the value of `p` in the inverse of the cdf for the Studentized range distribution with `nmeans` number of groups and `dof` degrees of freedom. 418 Only accurate to 4 decimal places. 419 420 ### jStat.weibull( scale, shape ) 421 422 #### jStat.weibull.pdf( x, scale, shape ) 423 424 Returns the value `x` in the pdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 425 426 #### jStat.weibull.cdf( x, scale, shape ) 427 428 Returns the value `x` in the cdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 429 430 #### jStat.weibull.inv( p, scale, shape ) 431 432 Returns the value of `x` in the inverse of the cdf for the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 433 434 #### jStat.weibull.mean( scale, shape ) 435 436 Returns the value of the mean of the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 437 438 #### jStat.weibull.median( scale, shape ) 439 440 Returns the value of the median of the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 441 442 #### jStat.weibull.mode( scale, shape ) 443 444 Returns the mode of the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 445 446 #### jStat.weibull.sample( scale, shape ) 447 448 Returns a random number whose distribution is the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 449 450 #### jStat.weibull.variance( scale, shape ) 451 452 Returns the variance of the Weibull distribution with parameters `scale` (lambda) and `shape` (k). 453 454 ### jStat.uniform( a, b ) 455 456 #### jStat.uniform.pdf( x, a, b ) 457 458 Returns the value of `x` in the pdf of the Uniform distribution from `a` to `b`. 459 460 #### jStat.uniform.cdf( x, a, b ) 461 462 Returns the value of `x` in the cdf of the Uniform distribution from `a` to `b`. 463 464 #### jStat.uniform.inv( p, a, b) 465 466 Returns the inverse of the `uniform.cdf` function; i.e. the value of `x` for which `uniform.cdf(x, a, b) == p`. 467 468 #### jStat.uniform.mean( a, b ) 469 470 Returns the value of the mean of the Uniform distribution from `a` to `b`. 471 472 #### jStat.uniform.median( a, b ) 473 474 Returns the value of the median of the Uniform distribution from `a` to `b`. 475 476 #### jStat.uniform.mode( a, b ) 477 478 Returns the value of the mode of the Uniform distribution from `a` to `b`. 479 480 #### jStat.uniform.sample( a, b ) 481 482 Returns a random number whose distribution is the Uniform distribution from `a` to `b`. 483 484 #### jStat.uniform.variance( a, b ) 485 486 Returns the variance of the Uniform distribution from `a` to `b`. 487 488 ### jStat.binomial 489 490 #### jStat.binomial.pdf( k, n, p ) 491 492 Returns the value of `k` in the pdf of the Binomial distribution with parameters `n` and `p`. 493 494 #### jStat.binomial.cdf( k, n, p ) 495 496 Returns the value of `k` in the cdf of the Binomial distribution with parameters `n` and `p`. 497 498 ### jStat.negbin 499 500 #### jStat.negbin.pdf( k, r, p ) 501 502 Returns the value of `k` in the pdf of the Negative Binomial distribution with parameters `n` and `p`. 503 504 #### jStat.negbin.cdf( x, r, p ) 505 506 Returns the value of `x` in the cdf of the Negative Binomial distribution with parameters `n` and `p`. 507 508 ### jStat.hypgeom 509 510 #### jStat.hypgeom.pdf( k, N, m, n ) 511 512 Returns the value of `k` in the pdf of the Hypergeometric distribution with parameters `N` (the population size), `m` (the success rate), and `n` (the number of draws). 513 514 #### jStat.hypgeom.cdf( x, N, m, n ) 515 516 Returns the value of `x` in the cdf of the Hypergeometric distribution with parameters `N` (the population size), `m` (the success rate), and `n` (the number of draws). 517 518 ### jStat.poisson 519 520 #### jStat.poisson.pdf( k, l ) 521 522 Returns the value of `k` in the pdf of the Poisson distribution with parameter `l` (lambda). 523 524 #### jStat.poisson.cdf( x, l ) 525 526 Returns the value of `x` in the cdf of the Poisson distribution with parameter `l` (lambda). 527 528 #### jStat.poisson.sample( l ) 529 530 Returns a random number whose distribution is the Poisson distribution with rate parameter l (lamda) 531 532 ### jStat.triangular 533 534 #### jStat.triangular.pdf( x, a, b, c ) 535 536 Returns the value of `x` in the pdf of the Triangular distribution with the parameters `a`, `b`, and `c`. 537 538 #### jStat.triangular.cdf( x, a, b, c ) 539 540 Returns the value of `x` in the cdf of the Triangular distribution with the parameters `a`, `b`, and `c`. 541 542 #### jStat.triangular.mean( a, b, c ) 543 544 Returns the value of the mean of the Triangular distribution with the parameters `a`, `b`, and `c`. 545 546 #### jStat.triangular.median( a, b, c ) 547 548 Returns the value of the median of the Triangular distribution with the parameters `a`, `b`, and `c`. 549 550 #### jStat.triangular.mode( a, b, c ) 551 552 Returns the value of the mode of the Triangular distribution with the parameters `a`, `b`, and `c`. 553 554 #### jStat.triangular.sample( a, b, c ) 555 556 Returns a random number whose distribution is the Triangular distribution with the parameters `a`, `b`, and `c`. 557 558 #### jStat.triangular.variance( a, b, c ) 559 560 Returns the value of the variance of the Triangular distribution with the parameters `a`, `b`, and `c`. 561 562 ### jStat.arcsine( a, b ) 563 564 #### jStat.arcsine.pdf( x, a, b ) 565 566 Returns the value of `x` in the pdf of the arcsine distribution from `a` to `b`. 567 568 #### jStat.arcsine.cdf( x, a, b ) 569 570 Returns the value of `x` in the cdf of the arcsine distribution from `a` to `b`. 571 572 #### jStat.arcsine.inv(p, a, b) 573 574 Returns the inverse of the `arcsine.cdf` function; i.e. the value of `x` for which `arcsine.cdf(x, a, b) == p`. 575 576 #### jStat.arcsine.mean( a, b ) 577 578 Returns the value of the mean of the arcsine distribution from `a` to `b`. 579 580 #### jStat.arcsine.median( a, b ) 581 582 Returns the value of the median of the arcsine distribution from `a` to `b`. 583 584 #### jStat.arcsine.mode( a, b ) 585 586 Returns the value of the mode of the arcsine distribution from `a` to `b`. 587 588 #### jStat.arcsine.sample( a, b ) 589 590 Returns a random number whose distribution is the arcsine distribution from `a` to `b`. 591 592 #### jStat.arcsine.variance( a, b ) 593 594 Returns the variance of the Uniform distribution from `a` to `b`.