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searching for Likelihood function 67 found (214 total)

alternate case: likelihood function

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 (4,595 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 ,
Indirect inference (324 words) [view diff] exact match in snippet view article find links to article
generative model with parameters θ {\displaystyle \theta } for which no likelihood function can easily be provided. Then we can ask the question of which choice
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 )
Ridge regression (4,146 words) [view diff] exact match in snippet view article find links to article
fits, this is valid, as long as the quadratic approximation of the likelihood function is valid. This means that, as long as the perturbation from the unregularised
Gamma distribution (9,097 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
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
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
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
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
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
Fisher's exact test (4,053 words) [view diff] exact match in snippet view article find links to article
marginal totals are (almost) ancillary implies that the appropriate likelihood function for making inferences about this odds ratio should be conditioned
Autoregressive model (5,421 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
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
Flow-based generative model (3,917 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
Cauchy distribution (6,933 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:
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
Positron emission tomography (8,845 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 (1,835 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
Multiple-try Metropolis (1,265 words) [view diff] exact match in snippet view article find links to article
Additionally, π ( x ) {\displaystyle \pi (\mathbf {x} )} is the likelihood function. Define w ( x , y ) = π ( x ) Q ( x , y ) λ ( x , y ) {\displaystyle
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
Independent component analysis (7,491 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
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
Heteroskedasticity-consistent standard errors (2,299 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)
Heteroskedasticity-consistent standard errors (2,299 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 (1,630 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
Generative adversarial network (13,881 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
Multiplicative function (3,626 words) [view diff] exact match in snippet view article find links to article
+{\frac {y_{t}^{2}}{g_{t}\tau }}\end{bmatrix}}} with a local likelihood function for y t 2 {\displaystyle y_{t}^{2}} with known g t {\displaystyle
Inverse Gaussian distribution (3,168 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
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)
Power law (8,187 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
Poisson distribution (11,215 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
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
Evacuation simulation (1,608 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
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)
Noise reduction (4,522 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
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})}
Complex normal distribution (2,289 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
Log-normal distribution (12,551 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 (13,328 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 (4,894 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 (6,579 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
Exponential tilting (3,858 words) [view diff] exact match in snippet view article find links to article
\ell (X)={\frac {d\mathbb {P} }{d\mathbb {P} _{\theta }}}} is the likelihood function. So, one samples from f θ {\displaystyle f_{\theta }} to estimate
Modified Kumaraswamy distribution (1,138 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 (35,733 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
Evacuation model (2,052 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
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
Kernel embedding of distributions (9,762 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}}