Likelihood

Mar 16, 2019

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Likelihood. Likelihood احتمال. apparent. apparent مبرهن. Literature. Literature. ادبيات. Lump sum. Lump sum. روي هم ، درهم. Attribute. Attribute ويژگي. Manner. Manner روش. Arrears. Arrears. عقب. As co-owner. As co-owner. به طور مشاع. Arbitrarily. Arbitrarily

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Likelihood

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Likelihood
Likelihood احتمال
apparent
apparent مبرهن
Literature
Literature ادبيات
Lump sum
Lump sum روي هم ، درهم
Attribute
Attribute ويژگي
Manner
Manner روش
Arrears
Arrears عقب
As co-owner
As co-owner به طور مشاع
Arbitrarily
Arbitrarily به طور دلخواه

Meanwhile
Meanwhile در اين بين
Merely
Merely صرفا
Mineral
Mineral معدني
Faithful
Faithful صادقانه
Minus
Minus منهاي
illustrate
illustrate توضيح دادن

Grasp
Grasp پي بردن به

Maximum likelihood (ML)

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Maximum Likelihood

Much estimation theory is presented in a rather ad hoc fashion. Minimising squared errors seems a good idea but why not minimise the absolute error or the cube of the absolute error?

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Likelihood Scores

Likelihood Scores. Lecture XX. Reminder from Information Theory. Mutual Information: Conditional Mutual Information: Entropy: Conditional Mutual Information: . Scoring Maximum Likelihood Function.

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Maximum Likelihood

Topics. DefinitionsDemonstration in EXCELExample of ML in ASREML. Definition. Maximum = ?????????Likelihood = ?????????????ML = ????????? ?????????. x2 = 1225. X = ?????? 2 ??? 1225???????????? ?????? ?????? ??????? 122510?10 = 100 ?????????? ????????????100?100 = 10000 ????????? ????????????

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Maximum Likelihood Estimation

Maximum Likelihood Estimation. Multivariate Normal distribution. The Method of Maximum Likelihood . Suppose that the data x 1 , … , x n has joint density function f ( x 1 , … , x n ; q 1 , … , q p )

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Likelihood methods

Likelihood methods. Given a particular model of evolution , we can estimate phylogenies using maximum likelihood. Likelihood with a coin. data ( D ). HHTTHTHHTHHTTT. L = Prob ( HHTTHTHHTHHTTT | p ) = p p (1-p ) (1-p ) p (1-p ) p p (1-p ) p p (1-p ) (1-p ) (1-p). = p 5 (1-p) 6.

276 views • 14 slides

Log Likelihood

Log Likelihood. Estimate the log likelihood in the KL basis, by rotating into the diagonal eigensystem, and rescaling with the square root of the eigenvalues Then C=1 at the fiducial basis We recompute C around this point – always close to a unit matrix Fisher matrix also simple.

667 views • 38 slides

Maximized Likelihood

Maximized Likelihood. Example 1. What is the maximized loglikelihood for the following structure? S: The parameters: P(A), P(B), P(C); d = 3 MLE : P(A=1)=1/2; P(B=1)=1/2; P(C=1)=3/4. Page 2. Example 1. The joint distribution represented by BN. Page 3. P(D| ).

256 views • 11 slides

Maximum Likelihood

Maximum Likelihood. We have studied the OLS estimator. It only applies under certain assumptions In particular, e ~ N(0, s 2 ) But what if the sampling distribution is not Normal? We can use an alternative estimator: MLE. See “Generalized Linear Models” in S-Plus. OLS vs. MLE.

280 views • 7 slides

Empirical Likelihood

Empirical Likelihood. Mai Zhou Department of Statistics University of Kentucky. A new (2001) book by A. Owen “Empirical Likelihood” . But Cox model with likelihood ratio output exists for a long time. SAS proc phreg , Splus/R function coxph( ) all have it computed.

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Maximum likelihood

Maximum likelihood. The maximum likelihood criterion. The optimal tree is that which would be most likely to give rise to the observed data (under a given model of evolution). An outline of the ML approach: Consider one character, i. (It is useful to arbitrarily root the tree).

553 views • 28 slides

The Likelihood Method

Marco Incagli â€“ INFN Pisa 24-5-2001 for the ppg group. The Likelihood Method. â€¦or how to distinguish electrons from pions in the energy range 300-500 MeV. this is evident in the MTRK distribution defined by the following equation:.

326 views • 18 slides

Likelihood methods

Likelihood methods. Trees - “What is the probability that a proposed model of sequence evolution and a particular tree would give rise to the observed data?” “What tree and model would maximize the probability of observing the observed data?. P (data) :: tree, model.

415 views • 26 slides

Empirical Likelihood

Empirical Likelihood. Mai Zhou Dept. of Statistics, University of Kentucky. Any first year Statistical Inference course will talk about (parametric) “likelihood function”. Three inference methods (tests) based on likelihood: 1. Wald test

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Maximum likelihood (cont.)

Maximum likelihood (cont.). Midterm. Relatedness – is not sharing a common ancestor but sharing a relatively recent CA Homoplasy includes convergence and/or reversal PTP looks at tree length ; g1 looks at skew in tree length Both introgression and LGT result in a dominant and minor history.

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Maximum Likelihood

Maximum Likelihood. Likelihood The likelihood is the probability of the data given the model. Likelihood. If we flip a coin and get a head and we think the coin is unbiased, then the probability of observing this head is 0.5.

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Maximum Likelihood

Maximum Likelihood. Benjamin Neale Boulder Workshop 2012. We will cover. Easy introduction to probability Rules of probability How to calculate likelihood for discrete outcomes Confidence intervals in likelihood Likelihood for continuous data. Starting simple.

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Likelihood

Likelihood. probability of observing the data given a model with certain parameters Maximum Likelihood Estimation (MLE) find the parameter combination that maximizes the likelihood requires some basic knowledge of probability. A Poisson Example. Pr(Observation= x ) = e - l l x / x !

262 views • 4 slides

Maximum Likelihood

Flips usage of probability function A typical calculation: P(h|n,p) = C(h, n) * p h * (1-p) (n-h) The implied question: Given p of success in single trial, what is probability of h success over n trials?. Maximum Likelihood. The ML question. The ML calculation:

470 views • 19 slides

Empirical Likelihood

Empirical Likelihood. Dario Nappa Jon Sanders. What we are going to talk about. Parametric Likelihood Empirical likelihood Empirical Likelihood Statistical properties Pseudo Empirical Likelihood Use of auxiliary information Comparison with other statistical confidence intervals

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Likelihood ratios

Likelihood ratios. Why these are the most thrilling statistics in Emergency Medicine. Objectives. Explore how we make diagnoses 2x2 tables, sensitivity and specificity Snout and Spin pre/post test probabilities and LRs. Revision of terms. Prevalence Sensitivity Specificity Truth table.

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