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Topic: Probability vector

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	Vector - Wikipedia, the free encyclopedia
		Vector (biology), a mechanism that transmits genes or organisms
		Vector, the capital of the Empire in the videogame Final Fantasy VI Vector the Crocodile, a character from the Sonic the Hedgehog series of videogames
		Vector (1970 novel), is a 1970 novel by Henry Sutton
	en.wikipedia.org /wiki/Vector (244 words)

	Probability
		Prior probability A prior probability is a evidence.
		Probability density function In random variable repeatedly and produces a histogram depicting relative frequencies of ou...
		Statistical probability " Statistical probability " is a term sometimes used informally as a synonym for probability wit...
	www.brainyencyclopedia.com /topics/probability.html (446 words)

	August 1999
		Geometric probability is a branch of mathematics that is concerned with the probabilities associated with geometric configurations of objects.
		, the probability that the needle intersects a tile boundary is 1.
		The probability that a polygon is missed during conversion to raster is clearly a function of raster cell size and polygon shape and area.
	www.ncgia.ucsb.edu /~ashton/pubs/geoprob/buff_rev.html (5389 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		The probability of an output vector (in an active state), is a function of the given input vector.
		Consequently, the probability that a bit in the registry is in an active state is 1:8.
		In the abstract it is stated that the probability distributions of the output vectors should be influenced by the input vector.
	www.ee.pdx.edu /~mperkows/CLASS_271/PROJECTS_99/michael-levy.html (1971 words)

	ipedia.com: Vector Article (Site not responding. Last check: 2007-10-29)
		The word vector means "carrier" in Latin; it is derived from the Latin verb vehere, which means to carry.
		Vector (spatial): In physics and engineering, vector most often refers specifically to an object that has a special relationship to the spatial coordinates/directions, i.e.
		Vector graphics describes a line or move in computer graphics
	www.ipedia.com /vector.html (328 words)

	Modeling Inheritance of Genetic Traits
		be a probability vector with six components, where component i is the probability that the mating pair chosen belongs to genotype class i.
		By choosing different genotype classes for the initial probability vector and repeating this process, we could generate probability vectors for the mating pair chosen from the first generation for each of the six possible genotype classes of the first mating pair.
		That is, assume that the initial mating pair belongs to genotype class 5 and compute the probability vector for the mating pair to be chosen from the first generation.
	www.math.wpi.edu /Course_Materials/MA2071A98/Projects/gene/node1.html (1009 words)

	[No title]
		The vector space is partitioned into N regions and every vector is replaced by the numerical identifier (id) of the region it belongs to.
		Instead, the data vectors are used to compute a mixture of N parametric distributions, usualy Gaussians, and each of these N distributions are visualized as representing the distribution of data within a particular partition.
		Once the vector was generated, and that underlying knowledge obscured from an observer, the observer could at best assign probabilities of the vector having been generated from each of the 3 Gaussians.
	www.cs.cmu.edu /~rsingh/sphinxman/tech2.html (920 words)

	AI: Population Based Incremental Learning in C# and .NET (Site not responding. Last check: 2007-10-29)
		The probability that we will generate a 1 or 0 in the first position is 50/50, so 50% of the chromosomes in the population have 1 in the first position, and 50% of the chromosomes in the population have 0 in the first position.
		The probability of generating a one in the 3rd and 4th position is 1, so this number will always be one.
		Generate a population of N chromosomes using the probability vector to randomly determine the gene in each chromosome position (1 or 0).
	www.c-sharpcorner.com /Code/2005/May/PBIL.asp (1336 words)

	Mean, Variance and Distributions
		The probability that the actual outcome will be less than or equal to 20 is (0.20+0.30), or 0.50, and the probability that the outcome will be less than or equal to 30 is 1.00.
		To produce a vector of these probabilities we can use the MATLAB cumsum function, which creates a new vector in which each element is the cumulative sum of all the elements up to and including the comparable position in the original vector.
		Thus the probability of a shortfall is 0.3.
	www.stanford.edu /~wfsharpe/mia/rr/mia_rr1.htm (3358 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		A "Probability Vector" is a vector whose entries are nonnegative and sum to 1.
		This experiment is a Markov experiment since probability of passing the state j from state i is independent of the state i-1 and i+1 and also probabilities are fixed for each experiment so this case is a Markov experiment.
		Thus the sum of the row vectors of T-I is the zero vector.
	www.iyte.edu.tr /~unalufuktepe/m144/markov/intro.htm (457 words)

	Linear Algebra Reading (Site not responding. Last check: 2007-10-29)
		Rich: A probabilty vector is a vector with nonnegative entries that add up to one.
		John S.: A steady-state vector for a stochastic matrix P is the probability vector q, such that P*q=q.
		A steady state vector for a matrix P is a special case of this.
	www.math.nyu.edu /~cowieson/Teaching/Linear/Read/week10.html (311 words)

	Application to Markov Chains
		If the initial distribution vector consist of numbers between 0 and 1, it tells you what proportion of the total number of objects are in each state in the beginning, and the elements in the column sum to one.
		X→p, where p is a fixed probability vector (the sum of its entries is 1), all of whose entries are positive.
		Remember that a steady-state vector is in particular a probability vector; that is the sum of its components is 1: 0.5t+t+t=1 gives t=0.4.
	aix1.uottawa.ca /~jkhoury/markov.htm (2477 words)

	Home
		Probability model based heuristics for optimisation and search problems have become popular recently, particularly in the community of evolutionary computation.
		A probability model is built based on global statistical information extracted from selected promising solutions.
		Then it receives a stochastic reward from the environment, which is used to update the probability vector.
	cswww.essex.ac.uk /staff/zhang/MoldeBasedWeb (287 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		% getEnt(A,B) returns the entropy for the joint distribution provided % by object matrix A and probability vector B. Each row of MxN matrix % A is an N-dimensional object, and probSet is a length-M vector of the % corresponding probabilities.
		Thus, the probability of object A(i,:) % is B(i).
		Otherwise, entropy only depends on the probability vector % B.) Matrix A need not be an exhaustive list of all possible objects % -- objects that do not appear are assumed to have zero probability.
	ruccs.rutgers.edu /~dfass/getEnt.m (187 words)

	AMS210 Homework 8
		Note the pivot columns of A consist of 4 linearly independent column vectors in R^5 which comprise a basis for Col A. Thus Col A is a 4 dimensional subspace of R^5.
		However, these pivot column vectors each have 5 entries (recall A has 5 rows) so are not in R^4.
		Thus the probability that the person is ill many days in future is the 2nd entry of steady state vector, i.e.
	www.ams.sunysb.edu /~yzhang/AMS210/spring_02/homework8.html (545 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		The output is a zero vector * with ones in the vector.
		The probability of ones in the vector is * controlled by the input vector prob[].
		* prob is the probability of ones in the output vector.
	www.clemson.edu /cle4_share/CWE/COES0915_CLUG/REFERENCE/matlabr14/toolbox/commblks/sim/sfun/scomrbitsrc2.c (261 words)

	Example
		The initial probability vector is then given by the "static" probability distribution (table 3.4).
		So the probabilities to find the subject in a given state without knowing in what state she is now is (cf.
		The probabilities to find her in a given state are the same as with the initial probability vector!
	tecfa.unige.ch /~lemay/thesis/THX-Doctorat/node57.html (361 words)

	Amino Acid Substitution Models
		This model of evolution is symmetric, i.e., the probability of having an i which mutates to a jis the same as starting with a j which mutates into an i.
		The diagonal elements of M are the probabilities that a given amino acid does not change, so (1-Mii) is the probability of mutating away from i.
		Or, if we start with amino acid i (a probability vector which contains a 1 in position i and 0s in all others) Mi (the ith column of M) is the corresponding probability* vector after one unit of random evolution.
	workshop.molecularevolution.org /resources/models/aamodels.php (844 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		However, in a quantum algorithm the probability vector is comprised of complex numbers.
		The probability of the system residing in any state is given by the square of the absolute value of the amplitude in that state.
		So, a probability vector of [0 1] would indicate a system in the "1" state, a vector of [1 0] could be in the "0" state, and a vector of [1/(2^.5) 1/(2^.5)] would be an even distribution between both states.
	www.nd.edu /~arodrig6/txt/05051518-2001.txt (2211 words)

	Phoneme Probability Estimation (Site not responding. Last check: 2007-10-29)
		The RNN has been trained using back propagation in time [10] on the TIMIT database [8] which is a database of phonetically labeled speech recorded from 630 adult male and female native English speakers from all the major dialect regions of the United States.
		The RNN produces a 40-dimensional phoneme probability vector (39 phoneme classes as defined by [5] and silence) every 10ms.
		One option is to classify each frame of audio by selecting the phoneme with the highest probability estimate from the RNN and displaying the symbol of this phoneme (this is similar to the approach taken in [2] to display phonemes through a vibrotactile output display).
	web.media.mit.edu /~dkroy/Assets98_HTML/node2.html (328 words)

	garbage_maxof(n) manual (Site not responding. Last check: 2007-10-29)
		The probability names description object, describing the relation between the model names and the respective output of the neural network.
		garbage maxof calculates for each probability vector the maximum of the defined set of models and appends this value to form a new output probability vector.
		For example suppose our neural network probability estimator has models for "s" and "n", then these can be used to model certain background events which correspond to these particular sounds.
	cslu.cse.ogi.edu /toolkit/old/man/n/garbage_maxof.html (367 words)

	Markov Systems
		The entries in a probability vector can represent the probabilities of finding a system in each of the states.
		If v is an initial distribution vector and P is the transition matrix for a Markov system, then the distribution vector after 1 step is the matrix product, vP.
		is the probability that the system will pass from state i to state j in n steps.
	www.zweigmedia.com /ThirdEdSite/Summary8.html (1226 words)

	The Graphical Display
		The probability vector produced by the RNN is connected to a graphical display.
		The display consists of a lattice of phoneme symbols as shown in Figure 1 (We use the same 39 phoneme classes as [4] and represent silence with an asterisk).
		most of the probability mass of the vector is assigned to relatively few phoneme classes) the spot light is bright and focused on only one or a few phoneme symbols.
	web.media.mit.edu /~dkroy/Assets98_HTML/node3.html (451 words)

	Edinburgh Mathematics Programme
		Vector geometry is used in giving a mathematical formulation of arrangements of objects in three dimensions.
		The theory of probability provides methods for measuring the uncertainty associated with combinations of uncertain events.
		Certain situations may be generalised to construct probability models with applications in many different areas.
	www.maths.ed.ac.uk /~derek/Syll/am2.html (507 words)

	Steady State Frequencies in Monopoly
		Let v be a row vector of length 40, with a one in the first position and zero elsewhere.
		You aren't sure of your location on the board, but you have probabilities for where you might be, and you want to know where you are going to wind up after your next turn.
		Your current location is represented by the probability vector v, whose entries sum to 1.
	www.mathreference.com /pr,steady.html (966 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		rand) statement creates a vector whose % elements are the row numbers j from the vector pus for which pus(j) > % rand.
		It then finds the minimum index from among % these, which in our example is 2, and sets that as the state at time 1 ib(1,1)=min(find(pus > rand)) ; % get initial random index.
		The % relevant conditional probability vector at time i, is the row from % p which corresponds to the state at time i-1 for i=2:(2sampsiz) ; ib(i,1)=min(find(ps(ib(i-1,1),:)' > rand)) ; end ; x=ib(sampsiz+1:2sampsiz,1) ; % extract the last half of the sample
	dge.repec.org /codes/marimon-scott/Burnside96/dspace.m (149 words)

	[No title]
		* There are two vectors to model the two strategies of paying to * immediately get out of jail and staying in as long as possible.
		These matrices hold, for each square, an array holding the * probability of reaching each other square on the board in one roll.
		* * These matrices are used to generate the final probability vectors by * taking a vector initialized with a probability of 1.0 in the Go square * and repeatedly multiplying by the appropriate matrix until the values * in the vector converges.
	www.tkcs-collins.com /truman/monopoly/mon_mark.c (494 words)

	Steps to Create a Bayesian Network (Site not responding. Last check: 2007-10-29)
		When the evidence is set, the probability of that state is set to one and the probability of the remaining states is set to zero.
		The second possible type of error could be associated to the probability tables for nodes.
		When using the vector of CBNSProbVectors, the number of CBNSProbVector objects should be same as the number of states of the node and the size of each vector should be equal to the product of the number of states of the parents.
	jmvidal.cse.sc.edu /targetshare/bayes (822 words)

	Speech Recognition
		An LPC encoding is a vector of features, for example, each formant represented by two numbers[features] plus two more for "spectral tilt".
		is the probability that the vector representation belonging to this particular time slice belongs to that phone.
		Viterbi(s,t) is the probability of the most likely path to s at t.
	www-rohan.sdsu.edu /~gawron/compling/speech.htm (694 words)

	Heavy-tailed asymptotics of stationary probability vectors of Markov chains of GI/G/1 type, Quan-Lin Li, Yiqiang Q. Zhao (Site not responding. Last check: 2007-10-29)
		In this paper, we provide a novel approach to studying the heavy-tailed asymptotics of the stationary probability vector of a Markov chain of GI/G/1 type, whose transition matrix is constructed from two matrix sequences referred to as a boundary matrix sequence and a repeating matrix sequence, respectively.
		We first provide a necessary and sufficient condition under which the stationary probability vector is heavy tailed.
		Based on this, we are able to provide a detailed analysis of the subexponential asymptotics of the stationary probability vector.
	projecteuclid.org /Dienst/UI/1.0/Display/euclid.aap/1118858635 (951 words)

	[No title] (Site not responding. Last check: 2007-10-29)
		The symbols can be represented as a % numeric vector or single-dimensional alphanumeric cell array.
		N is an integer % between 2 and 10 (inclusive) that must not exceed the number of % source symbols whose probabilities appear in PROB.
		If there exist nodes with the % same probability as the new node, the new node is placed before these % same value nodes.
	www.clemson.edu /cle4_share/CWE/COES0915_CLUG/REFERENCE/matlabr14/toolbox/comm/comm/huffmandict.m (441 words)

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