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searching for Likelihood function 61 found (215 total)

Point estimation (2,284 words) [view diff] exact match in snippet view article find links to article

the likelihood function. It uses a known model (ex. the normal distribution) and uses the values of parameters in the model that maximize a likelihood function

Behavioral modeling in hydrology (133 words) [view diff] exact match in snippet view article find links to article

frequent case, has to be inferred from the available information and a likelihood function that encodes the probability of some assumed behaviors. This modeling

Bernoulli distribution (2,196 words) [view diff] case mismatch in snippet view article find links to article

{\displaystyle {\begin{aligned}I(p)={\frac {1}{pq}}\end{aligned}}} Proof: The Likelihood Function for a Bernoulli random variable X {\displaystyle X} is: L ( p ; X

Point process (4,599 words) [view diff] no match in snippet view article find links to article

In statistics and probability theory, a point process or point field is a set of a random number of mathematical points randomly located on a mathematical

Dynamic unobserved effects model (1,406 words) [view diff] exact match in snippet view article find links to article

of the likelihood function: treating them as constant or imposing a distribution on them and calculate out the unconditional likelihood function. But whichever

Multivariate probit model (1,493 words) [view diff] exact match in snippet view article find links to article

_{2}<-X_{2}\beta _{2}){\Big )}.\end{aligned}}} After some rewriting, the log-likelihood function becomes: ∑ ( Y 1 Y 2 ln ⁡ Φ ( X 1 β 1 , X 2 β 2 , ρ ) + ( 1 − Y 1

Mode choice (2,728 words) [view diff] exact match in snippet view article find links to article

obtaining our sample over a range of γ – this is our likelihood function. The likelihood function for n independent observations in a logit model is L

Truncated normal hurdle model (1,034 words) [view diff] exact match in snippet view article find links to article

Truncated Normal Hurdle Model is usually estimated through MLE. The log-likelihood function can be written as: ℓ ( β , γ , σ ) = ∑ i = 1 N 1 [ y i = 0 ] log

Dirichlet-multinomial distribution (6,950 words) [view diff] no match in snippet view article find links to article

In probability theory and statistics, the Dirichlet-multinomial distribution is a family of discrete multivariate probability distributions on a finite

Independent and identically distributed random variables (2,119 words) [view diff] exact match in snippet view article find links to article

distribution simplifies the calculation of the likelihood function. Due to this assumption, the likelihood function can be expressed as: l ( θ ) = P ( x 1 ,

Bayesian operational modal analysis (1,100 words) [view diff] exact match in snippet view article find links to article

parameters in a Bayesian method is equal to the location where the likelihood function is maximized, which is the estimate in Maximum Likelihood Method

Probit model (3,260 words) [view diff] exact match in snippet view article find links to article

(x_{i}^{\operatorname {T} }\beta )]^{(1-y_{i})}\right)} The joint log-likelihood function is thus ln ⁡ L ( β ; Y , X ) = ∑ i = 1 n ( y i ln ⁡ Φ ( x i T β )

Proofs involving ordinary least squares (5,246 words) [view diff] exact match in snippet view article find links to article

unknown parameters in a statistical model by constructing a log-likelihood function corresponding to the joint distribution of the data, then maximizing

Gamma distribution (9,095 words) [view diff] exact match in snippet view article find links to article

standard Weibull distribution of shape α {\displaystyle \alpha } . The likelihood function for N iid observations (x1, ..., xN) is L ( α , θ ) = ∏ i = 1 N f

Separation (statistics) (647 words) [view diff] exact match in snippet view article

maximum likelihood (ML) estimation relies on maximization of the likelihood function, where e.g. in case of a logistic regression with completely separated

Fisher consistency (765 words) [view diff] exact match in snippet view article find links to article

_{i=1}^{n}\mu =\mu ,} so we have Fisher consistency. Maximising the likelihood function L gives an estimate that is Fisher consistent for a parameter b if

Difference density map (576 words) [view diff] exact match in snippet view article find links to article

factor. These coefficients are derived from the gradient of the likelihood function of the observed structure factors on the basis of the current model

Generalized inverse Gaussian distribution (1,357 words) [view diff] exact match in snippet view article find links to article

1 , … , x T {\displaystyle X=x_{1},\ldots ,x_{T}} , with normal likelihood function, conditioned on z : {\displaystyle z:} P ( X ∣ z , α , β ) = ∏ i

Wrapped Cauchy distribution (2,035 words) [view diff] exact match in snippet view article find links to article

Press.[page needed] J. Copas (1975). "On the unimodality of the likelihood function for the Cauchy distribution". Biometrika. 62 (3): 701–704. doi:10

Autoregressive model (5,837 words) [view diff] exact match in snippet view article find links to article

(broadly equivalent to the forward prediction least squares scheme) the likelihood function considered is that corresponding to the conditional distribution

Dynamic discrete choice (2,949 words) [view diff] exact match in snippet view article find links to article

of the likelihood function, a special case of mathematical programming with equilibrium constraints (MPEC). Specifically, the likelihood function is maximized

Cauchy distribution (6,910 words) [view diff] exact match in snippet view article find links to article

efficient, it is relatively inefficient for small samples. The log-likelihood function for the Cauchy distribution for sample size n {\displaystyle n} is:

Positron emission tomography (8,760 words) [view diff] exact match in snippet view article find links to article

Research has shown that Bayesian methods that involve a Poisson likelihood function and an appropriate prior probability (e.g., a smoothing prior leading

Sequential probability ratio test (1,847 words) [view diff] exact match in snippet view article find links to article

=\theta _{1}\end{cases}}\qquad \theta _{1}>\theta _{0}.} Then the log-likelihood function (LLF) for one sample is log ⁡ Λ ( x ) = log ⁡ ( θ 1 − 1 e − x θ 1

Independent component analysis (7,462 words) [view diff] exact match in snippet view article find links to article

the model parameter values given the observed data. We define a likelihood function L ( W ) {\displaystyle \mathbf {L(W)} } of W {\displaystyle \mathbf

Iterative reconstruction (1,784 words) [view diff] exact match in snippet view article find links to article

to expectation-maximization-based methods which involve a Poisson likelihood function only. As another example, it is considered superior when one does

Vladimir Varyukhin (1,416 words) [view diff] exact match in snippet view article find links to article

- 2260 V. A. Varyuhin, V. I. Pokrovskii, V. F. Sakhno, “Modified likelihood function in the problem of the source angular coordinate determination using

Markov switching multifractal (1,572 words) [view diff] exact match in snippet view article find links to article

\left[-{\frac {(r_{t}-\mu )^{2}}{2\sigma ^{2}(m^{i})}}\right].} The log likelihood function has the following analytical expression: ln ⁡ L ( r 1 , … , r T ;

Heteroskedasticity-consistent standard errors (2,298 words) [view diff] exact match in snippet view article find links to article

biased (in an unknown direction), as well as inconsistent (unless the likelihood function is modified to correctly take into account the precise form of heteroskedasticity)

Surendra Prasad (1,638 words) [view diff] exact match in snippet view article find links to article

Indian National Science Academy. Agrawal M., S. (2000). "A modified likelihood function approach to DOA estimation in the presence of unknown spatially correlated

Inverse Gaussian distribution (3,166 words) [view diff] exact match in snippet view article find links to article

wi known, (μ, λ) unknown and all Xi independent has the following likelihood function L ( μ , λ ) = ( λ 2 π ) n 2 ( ∏ i = 1 n w i X i 3 ) 1 2 exp ⁡ ( λ

Generative adversarial network (13,865 words) [view diff] exact match in snippet view article find links to article

generative models, which means that they do not explicitly model the likelihood function nor provide a means for finding the latent variable corresponding

List of named matrices (1,336 words) [view diff] exact match in snippet view article find links to article

partial derivative, with respect to a parameter, of the log of the likelihood function of a random variable. Hat matrix — a square matrix used in statistics

Richardson–Lucy deconvolution (2,155 words) [view diff] exact match in snippet view article find links to article

maximum likelihood estimation the aim is to locate the maximum of the likelihood function without concern for its absolute value. ln ⁡ ( P ( m | E ) ) = ∑

Split normal distribution (1,344 words) [view diff] exact match in snippet view article find links to article

the parameters using maximum likelihood method. He shows that the likelihood function can be expressed in an intensive form, in which the scale parameters

Nonlinear mixed-effects model (3,677 words) [view diff] exact match in snippet view article find links to article

background knowledge and prior elicitation; (b)–(ii) determining the likelihood function based on a nonlinear function f {\displaystyle f} ; and (b)–(iii)

Auxiliary particle filter (2,156 words) [view diff] exact match in snippet view article find links to article

_{i}}},\omega _{j}=f(y|\alpha ^{j})} . The weights represent the likelihood function f ( y t + 1 | α t + 1 ) {\displaystyle f(y_{t+1}|\alpha _{t+1})}

Evacuation simulation (1,669 words) [view diff] exact match in snippet view article find links to article

field cellular automaton models for pedestrian dynamics by using likelihood function optimization". Physica A: Statistical Mechanics and its Applications

Domain adaptation (1,614 words) [view diff] exact match in snippet view article find links to article

predictive inference under covariate shift by weighting the log-likelihood function". Journal of Statistical Planning and Inference. 90 (2): 227–244

Poisson distribution (11,402 words) [view diff] exact match in snippet view article find links to article

function for the Poisson population, we can use the logarithm of the likelihood function: ℓ ( λ ) = ln ⁡ ∏ i = 1 n f ( k i ∣ λ ) = ∑ i = 1 n ln ( e − λ λ

Multilevel model (4,923 words) [view diff] exact match in snippet view article find links to article

background knowledge and prior elicitation; (b)–(ii) determining the likelihood function based on a nonlinear function f {\displaystyle f} ; and (b)–(iii)

Power law (8,435 words) [view diff] exact match in snippet view article find links to article

normalized. Given a choice for x min {\displaystyle x_{\min }} , the log likelihood function becomes: L ( α ) = log ⁡ ∏ i = 1 n α − 1 x min ( x i x min ) − α

Noise reduction (4,471 words) [view diff] exact match in snippet view article find links to article

image data as a Bayesian prior and the auto-normal density as a likelihood function, with the resulting posterior distribution offering a mean or mode

Complex normal distribution (2,268 words) [view diff] exact match in snippet view article find links to article

covariance matrix Γ {\displaystyle \Gamma } are unknown, a suitable log likelihood function for a single observation vector z {\displaystyle z} would be ln ⁡

Multispecies coalescent process (5,675 words) [view diff] exact match in snippet view article find links to article

i ) {\displaystyle f(D\mid G)=\prod _{i}f(D_{i}\mid G_{i})} The likelihood function or the probability of the sequence data given the parameters Θ {\displaystyle

Flow-based generative model (9,669 words) [view diff] exact match in snippet view article find links to article

and generative adversarial network do not explicitly represent the likelihood function. Let z 0 {\displaystyle z_{0}} be a (possibly multivariate) random

Log-normal distribution (12,545 words) [view diff] exact match in snippet view article find links to article

{\displaystyle {\mathcal {N}}(\mu ,\sigma ^{2})} . Therefore, the log-likelihood function is ℓ ( μ , σ ∣ x 1 , x 2 , … , x n ) = − ∑ i ln ⁡ x i + ℓ N ( μ

Pierre-Simon Laplace (13,298 words) [view diff] exact match in snippet view article find links to article

observations, then the least squares estimates would not only maximise the likelihood function, considered as a posterior distribution, but also minimise the expected

Regularized least squares (4,910 words) [view diff] exact match in snippet view article find links to article

this, first note that the OLS objective is proportional to the log-likelihood function when each sampled y i {\displaystyle y^{i}} is normally distributed

Statistical association football predictions (2,907 words) [view diff] exact match in snippet view article find links to article

the team strengths can be estimated by minimizing the negative log-likelihood function with respect to λ {\displaystyle \lambda } and μ {\displaystyle \mu

Machine olfaction (3,129 words) [view diff] exact match in snippet view article find links to article

{\displaystyle \theta } is the estimated odor source position, and the log likelihood function is L ( θ ) ∼ 1 2 ∑ i = 1 N ‖ Z i − γ i c d i 2 ‖ = 1 2 ∑ i = 1 N

Item response theory (6,585 words) [view diff] exact match in snippet view article find links to article

multiplying the item response function for each item to obtain a likelihood function, the highest point of which is the maximum likelihood estimate of

Hyperbolastic functions (7,041 words) [view diff] exact match in snippet view article find links to article

{\displaystyle {\boldsymbol {\beta }}} can be obtained by maximizing the log-likelihood function β ^ = argmax β ∑ i = 1 n [ y i l n ( π ( x i ; β ) ) + ( 1 − y i

Minimum mean square error (9,310 words) [view diff] exact match in snippet view article find links to article

density, p ( y k | x k ) {\displaystyle p(y_{k}|x_{k})} is called the likelihood function, and p ( x k | y 1 , … , y k − 1 ) {\displaystyle p(x_{k}|y_{1},\ldots

Modified Kumaraswamy distribution (1,145 words) [view diff] exact match in snippet view article find links to article

method for parameter estimation of the MK distribution. The log-likelihood function for the MK distribution, given a sample x 1 , … , x n {\displaystyle

List of agnostics (35,734 words) [view diff] exact match in snippet view article find links to article

Brownian motion. Thiele introduced the cumulants and (in Danish) the likelihood function; these contributions were not credited to Thiele by Ronald A. Fisher

Omnibus test (6,180 words) [view diff] exact match in snippet view article find links to article

{L(y_{i}|\theta _{0})}{L(y_{i}|\theta _{1})}}} , where L(yi|θ) is the likelihood function, which refers to the specific θ. The numerator corresponds to the

Evacuation model (2,116 words) [view diff] exact match in snippet view article find links to article

field cellular automaton models for pedestrian dynamics by using likelihood function optimization". Physica A: Statistical Mechanics and Its Applications

Kernel embedding of distributions (9,770 words) [view diff] exact match in snippet view article find links to article

distribution can be expressed in terms of a prior distribution and a likelihood function as Q ( Y ∣ x ) = P ( x ∣ Y ) π ( Y ) Q ( x ) {\displaystyle Q(Y\mid

Innovation method (6,480 words) [view diff] exact match in snippet view article find links to article

for the parameters of the SDE (1) is the one that maximizes the likelihood function of the discrete-time innovation process { ν t k } k = 1 , … , M −

Neyman Type A distribution (3,522 words) [view diff] exact match in snippet view article find links to article

Where likelihood L ( ) {\displaystyle {\mathcal {L}}()} is the log-likelihood function. W does not have an asymptotic χ 1 2 {\displaystyle \chi _{1}^{2}}