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Method of estimating the parameters of a statistical model
statistics, the maximum a posteriori (MAP) estimate of an unknown quantity is the mode of the posterior density. The MAP can be used to obtain a point estimate
Maximum a posteriori estimation
Maximum_a_posteriori_estimation
Mathematical decision rule
Bayesian statistics is maximum a posteriori estimation. Suppose an unknown parameter θ {\displaystyle \theta } is known to have a prior distribution π {\displaystyle
Bayes_estimator
Method of estimating the parameters of a statistical model, given observations
to maximum a posteriori (MAP) estimation with a prior distribution that is uniform in the region of interest. In frequentist inference, MLE is a special
Maximum_likelihood_estimation
Algorithm for analyzing noisy data streams
maximum a posteriori estimation is formally the application of the maximum a posteriori (MAP) estimation approach. This is more complex than maximum likelihood
Maximum likelihood sequence estimation
Maximum_likelihood_sequence_estimation
Branch of statistics
distribution. Posterior median estimation: The estimator takes the median of the posterior distribution. Maximum à-posteriori estimation (MAP): The estimator takes
Parametric_statistics
Compartmental and kinetic modeling software
independent analysis, and a Bayesian maximum a posteriori estimation that improves parameter fitting when data are noisy. SAAM II offers a user-friendly interface
SAAM_II
Conditional probability used in Bayesian statistics
various point and interval estimates can be derived, such as the maximum a posteriori (MAP) or the highest posterior density interval (HPDI). But while conceptually
Posterior_probability
Method of statistical inference
g., by maximum likelihood or maximum a posteriori estimation (MAP)—and then plugging this estimate into the formula for the distribution of a data point
Bayesian_inference
Principle in Bayesian statistics
principle of maximum entropy. One of the main applications of the maximum entropy principle is in discrete and continuous density estimation. Similar to
Principle_of_maximum_entropy
Results about asymptotic posterior normality
conditions, a posterior distribution converges in total variation distance to a multivariate normal distribution centered at the maximum likelihood estimator
Bernstein–von_Mises_theorem
Signal-processing procedure
problem Regularization (mathematics) Blind equalization Maximum a posteriori estimation Maximum likelihood ImageJ plugin for deconvolution Barmby, Pauline;
Blind_deconvolution
Theory and paradigm of statistics
proportional to this product: P ( A ∣ B ) ∝ P ( B ∣ A ) P ( A ) {\displaystyle P(A\mid B)\propto P(B\mid A)P(A)} The maximum a posteriori, which is the mode of the
Bayesian_statistics
Computational navigational technique used by robots and autonomous vehicles
a set which encloses the pose of the robot and a set approximation of the map. Bundle adjustment, and more generally maximum a posteriori estimation (MAP)
Simultaneous localization and mapping
Simultaneous_localization_and_mapping
Parameter estimation via sample statistics
statistics, point estimation involves the use of sample data to calculate a single value (known as a point estimate, since it identifies a point rather than
Point_estimation
Branch of statistics to estimate models based on measured data
squared error (MMSE), also known as Bayes least squared error (BLSE) Maximum a posteriori (MAP) Minimum variance unbiased estimator (MVUE) Nonlinear system
Estimation_theory
Mathematical rule for inverting probabilities
P ( A | B ) = P ( B | A ) P ( A ) P ( B | A ) P ( A ) + P ( B | ¬ A ) P ( ¬ A ) . {\displaystyle P(A|B)={\frac {P(B|A)P(A)}{P(B|A)P(A)+P(B|\neg A)P(\neg
Bayes'_theorem
Statistical concept
or maximum a posteriori estimation (MAP). Generally these methods consider separately the questions of system identification and parameter estimation; methods
Mixture_model
Criterion for model selection
_{n}(\theta )} is the maximum a posteriori (MAP) estimate of θ {\displaystyle \theta } . Note that it is always possible to select a prior density π ( θ
Bayesian information criterion
Bayesian_information_criterion
Optimization method
automatic differentiation as an option to solve maximum likelihood estimation and maximum a posteriori estimation problems. Notable proprietary implementations
Broyden–Fletcher–Goldfarb–Shanno algorithm
Broyden–Fletcher–Goldfarb–Shanno_algorithm
Mathematical methods used in Bayesian inference and machine learning
from maximum likelihood (ML) or maximum a posteriori (MAP) estimation of the single most probable value of each parameter to fully Bayesian estimation which
Variational_Bayesian_methods
Probability rule of thumb
assessing the likelihood that tossing a coin will result in either a head or a tail facing upwards, there is a possibility, albeit remote, that the coin
Cromwell's_rule
Topics referred to by the same term
a representation of a topological subdivision of the plane Functional predicate, in formal logic Maximum a posteriori estimation, in statistics Markov
Map_(disambiguation)
Optimization algorithm
automatic differentiation as an option to solve maximum likelihood estimation and maximum a posteriori estimation problems. Notable non open source implementations
Limited-memory_BFGS
Monte Carlo algorithm
occurs most commonly; this is essentially equivalent to maximum a posteriori estimation of a parameter. (Since the parameters are usually continuous,
Gibbs_sampling
Calculation of complex statistical distributions
effect of correlation on estimation can be quantified through the Markov chain central limit theorem. For a chain targeting a distribution with variance
Markov_chain_Monte_Carlo
Algorithm that estimates unknowns from a series of measurements over time
below. The Kalman filter is a minimum mean-square error (MMSE) estimator. The error in the a posteriori state estimation is x k − x ^ k ∣ k {\displaystyle
Kalman_filter
Process of using data analysis for predicting population data from sample data
descriptive complexity), MDL estimation is similar to maximum likelihood estimation and maximum a posteriori estimation (using maximum-entropy Bayesian priors)
Statistical_inference
Derivation of the laws of probability theory
Rationality and Consistency to Bayesian Probability". In Skilling, John (ed.). Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer. pp. 29–44. doi:10
Cox's_theorem
Classification algorithm in statistics
{\displaystyle P_{r}} denotes a probability distribution. A classifier is a rule that assigns to an observation X=x a guess or estimate of what the unobserved
Bayes_classifier
Distribution of an uncertain quantity
determining a non-informative prior is the principle of indifference, which assigns equal probabilities to all possibilities. In parameter estimation problems
Prior_probability
Concept in statistics
distribution function etc. Parameter estimation (maximum-likelihood or maximum-a-posteriori estimation) within a compound distribution model may sometimes
Compound probability distribution
Compound_probability_distribution
Concept in probability theory
time. For related approaches, see Recursive Bayesian estimation and Data assimilation. Suppose a rental car service operates in your city. Drivers can
Conjugate_prior
Lower bound on the log-likelihood of some observed data
variational free energy) is a useful lower bound on the log-likelihood of some observed data. The ELBO is useful because it provides a guarantee on the worst-case
Evidence_lower_bound
In Bayesian probability theory
A marginal likelihood is a likelihood function that has been integrated over the parameter space. In Bayesian statistics, it represents the probability
Marginal_likelihood
Statistical model written in multiple levels
ISBN 1-58488-388-X. Lee, Se Yoon; Lei, Bowen; Mallick, Bani (2020). "Estimation of COVID-19 spread curves integrating global data and borrowing information"
Bayesian hierarchical modeling
Bayesian_hierarchical_modeling
Probability estimate
phrase a-posteriori probability is also used as an alternative to "empirical probability" or "relative frequency". The use of the phrase "a-posteriori" is
Empirical_probability
American scientist and IEEE fellow
was Resolution enhancement of hyperspectral imagery using maximum a posteriori estimation with a stochastic mixing model. Eismann is Chief Scientist at the
Michael_Eismann
Probabilistic graphical representation of causal relationships
regularity conditions, this process converges on maximum likelihood (or maximum posterior) values for parameters. A more fully Bayesian approach to parameters
Bayesian_network
Technique to make a model more generalizable and transferable
Springer. ISBN 978-0-387-31073-2. For the connection between maximum a posteriori estimation and ridge regression, see Weinberger, Kilian (July 11, 2018)
Regularization_(mathematics)
Distribution of new data marginalized over the posterior
observed values. Given a set of N i.i.d. observations X = { x 1 , … , x N } {\displaystyle \mathbf {X} =\{x_{1},\dots ,x_{N}\}} , a new value x ~ {\displaystyle
Posterior predictive distribution
Posterior_predictive_distribution
Bayesian statistical inference method
parametric empirical Bayes point estimation, is to approximate the marginal using the maximum likelihood estimate (MLE), or a moments expansion, which allows
Empirical_Bayes_method
Iterative method for finding maximum likelihood estimates in statistical models
(EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where
Expectation–maximization algorithm
Expectation–maximization_algorithm
Computational method in Bayesian statistics
wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years
Approximate Bayesian computation
Approximate_Bayesian_computation
Function related to statistics and probability theory
on the actual data points, it becomes a function solely of the model parameters. In maximum likelihood estimation, the model parameter(s) or argument that
Likelihood_function
Interpretation of probability
). Maximum Entropy and Bayesian Methods. Dordrecht: Kluwer. pp. 29–44. doi:10.1007/978-94-015-7860-8_2. ISBN 0-7923-0224-9. Halpern, J. (1999). "A counterexample
Bayesian_probability
coefficient Maximum a posteriori estimation Maximum entropy classifier – redirects to Logistic regression Maximum-entropy Markov model Maximum entropy method –
List_of_statistics_articles
Type of "good" decision rule in Bayesian statistics
mean-squared-error loss function. Thus least squares estimation is not an admissible estimation procedure in this context. Some others of the standard
Admissible_decision_rule
Optimization technique
solution corresponds to the maximum a posteriori estimate of a solution. Although many computer vision algorithms involve cutting a graph (e.g. normalized
Graph cuts in computer vision and artificial intelligence
Graph_cuts_in_computer_vision_and_artificial_intelligence
most likely answer (i.e. the maximum a posteriori (MAP) state). The "softening" of the logical formulas makes inference a polynomial time operation rather
Probabilistic_soft_logic
Signal processing filter
needed] Wiener filter Norbert Wiener Wiener deconvolution Maximum a posteriori estimation Pratt, William K. (July 1972). "Generalized Wiener Filtering
Generalized_Wiener_filter
In probability theory, a rule for assigning epistemic probabilities
as an application of the principle of parsimony and as a special case of the principle of maximum entropy. In Bayesian probability, this is the simplest
Principle_of_indifference
Partitioning a digital image into segments
identifying a labelling scheme given a particular set of features are detected in the image. This is a restatement of the maximum a posteriori estimation method
Image_segmentation
Calculation of position based on satellite signal timing
{\hat {t}}_{\text{rec}})} . Their inference is formalized as maximum a posteriori estimation. The posterior distribution of r rec {\displaystyle {\boldsymbol
Satellite_navigation_solution
Parameter estimation technique in statistics
In statistics, the method of moments is a method of estimation of population parameters. The same principle is used to derive higher moments like skewness
Method of moments (statistics)
Method_of_moments_(statistics)
Probabilistic theory of knowledge
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory
Bayesian_epistemology
Proposition in statistics
supported by the evidence. This is one basis for the widely used method of maximum likelihood. The likelihood principle was first identified by that name
Likelihood_principle
Analytical expression in statistics
{\hat {\theta }}} is the location of a mode of the joint target density, also known as the maximum a posteriori or MAP point and S − 1 {\displaystyle
Laplace's_approximation
Method of statistical analysis
regression. A similar analysis can be performed for the general case of the multivariate regression and part of this provides for Bayesian estimation of covariance
Bayesian_linear_regression
Method for numerical integration
list (link) Walter, Clement (2017). "Point-process based Monte Carlo estimation". Statistics and Computing. 27: 219–236. arXiv:1412.6368. doi:10.1007/s11222-015-9617-y
Nested_sampling_algorithm
Thought experiment, to justify Bayesian probability
are a set of results showing that agents must satisfy the axioms of rational choice to avoid a kind of self-contradiction called a Dutch book. A Dutch
Dutch_book_arguments
Class of statistical models
setting, there exist several methods for doing maximum-likelihood estimation or maximum a posteriori estimation in certain classes of nonlinear mixed-effects
Nonlinear_mixed-effects_model
I)+\log \pi _{\operatorname {Diff} _{V}}(\phi )\ .} Maximum a posteriori estimation (MAP) estimation is central to modern statistical theory. Parameters
Bayesian estimation of templates in computational anatomy
Bayesian_estimation_of_templates_in_computational_anatomy
Concept in Bayesian statistics
ISBN 0-340-52922-9 Chen, Ming-Hui; Shao, Qi-Man (1 March 1999). "Monte Carlo Estimation of Bayesian Credible and HPD Intervals". Journal of Computational and
Credible_interval
Ratio of competing statistical models
the maximum likelihood estimate of the parameter for each statistical model is used, then the test becomes a classical likelihood-ratio test. Unlike a likelihood-ratio
Bayes_factor
Statistical estimation technique
{\varepsilon }}|\mathbf {b} )} is the log-likelihood. The maximum a posteriori (MAP) estimate is then the maximum likelihood estimate (MLE), which is equivalent
Generalized_least_squares
\operatorname {Exp} _{\mathrm {id} }(v)\cdot I_{a})\pi _{V}(dv)\ .} Maximum a posteriori estimation (MAP) estimation is central to modern statistical theory.
Bayesian model of computational anatomy
Bayesian_model_of_computational_anatomy
Information scientist
Gold Award, 1997 Gauvain, J. L.; Chin-Hui, Lee (April 1994). "Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains"
Chin-Hui_Lee
Inference algorithm for probabilistic graphical models
can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables. The algorithm
Variable_elimination
Methodology for assigning prior probabilities
Justice, James H. (ed.). Maximum Entropy and Bayesian Methods in Applied Statistics. Fourth Annual Workshop on Bayesian/Maximum Entropy Methods. Cambridge
Principle of transformation groups
Principle_of_transformation_groups
Class of digital signal-processing methods
if noise-predictive detection is performed in conjunction with a maximum a posteriori (MAP) detection algorithm such as the BCJR algorithm then NPML and
Noise-predictive maximum-likelihood detection
Noise-predictive_maximum-likelihood_detection
Experimental design framework
Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It
Bayesian_experimental_design
In Bayesian statistics, a hyperprior is a prior distribution on a hyperparameter, that is, on a parameter of a prior distribution. As with the term hyperparameter
Hyperprior
Parameter of a prior distribution in Bayesian statistics
In Bayesian statistics, a hyperparameter is a parameter of a prior distribution; the term is used to distinguish them from parameters of the model for
Hyperparameter (Bayesian statistics)
Hyperparameter_(Bayesian_statistics)
Optimality criterion in phylogeny
taxa. Maximum parsimony is used with most kinds of phylogenetic data; until recently, it was the only widely used character-based tree estimation method
Maximum_parsimony
Statistical model for a binary dependent variable
coefficients. The use of a regularization condition is equivalent to doing maximum a posteriori (MAP) estimation, an extension of maximum likelihood. (Regularization
Logistic_regression
Error correction algorithm
The Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is an algorithm for maximum a posteriori decoding of error correcting codes defined on trellises (principally
BCJR_algorithm
Regression for more than two discrete outcomes
βk are typically jointly estimated by maximum a posteriori (MAP) estimation, which is an extension of maximum likelihood using regularization of the
Multinomial logistic regression
Multinomial_logistic_regression
Analog of Pareto efficiency for situations with incomplete information
there is incomplete information. Under Pareto efficiency, an allocation of a resource is Pareto efficient if there is no other allocation of that resource
Bayesian_efficiency
Statistics concept
phases: a prediction phase and an estimation phase: During the prediction phase, the state is predicted using the dynamic model and the estimation of the
Bayesian_programming
Adaptive filter algorithm for digital signal processing
type-II maximum likelihood estimation the optimal λ {\displaystyle \lambda } can be estimated from a set of data. The discussion resulted in a single equation
Recursive least squares filter
Recursive_least_squares_filter
Means to measure signal processing ability
case L ( y ) ≥ τ M A P {\displaystyle L(y)\geq \tau _{MAP}} . This is called MAP testing, where MAP stands for "maximum a posteriori"). Taking this approach
Detection_theory
Probability distribution
plays a central role in maximum likelihood estimation, see section "Parameter estimation, maximum likelihood." Actually, when performing maximum likelihood
Beta_distribution
Form of computer-based test that adapts to the examinee's ability level
assumes an a priori distribution of examinee ability, and has two commonly used estimators: expectation a posteriori and maximum a posteriori. Maximum likelihood
Computerized_adaptive_testing
Unit of information
Data (/ˈdeɪtə/ DAY-tə, US also /ˈdætə/ DAT-ə, India: /ˈdiːtə/ DEE-tə) is a collection of discrete or continuous values that conveys information, describing
Data
Rule for calculating an estimate of a given quantity based on observed data
(BLUE) Empirical measure Estimation theory Invariant estimator Kalman filter Markov chain Monte Carlo (MCMC) Maximum a posteriori (MAP) Method of moments
Estimator
Generating high-resolution video frames from given low-resolution ones
the task. maximum likelihood (ML) methods estimate more probable image. Another group of methods use maximum a posteriori (MAP) estimation. Regularization
Video_super-resolution
Problem in statistics
achieves its peak at r = h / (h + t) = 0.7; this value is called the maximum a posteriori (MAP) estimate of r. Also with the uniform prior, the expected value
Checking whether a coin is fair
Checking_whether_a_coin_is_fair
Automated recognition of patterns and regularities in data
{\displaystyle {\boldsymbol {\theta }}} is typically learned using maximum a posteriori (MAP) estimation. This finds the best value that simultaneously meets two
Pattern_recognition
Technique for the generative modeling of a continuous probability distribution
| y ) {\displaystyle p(x|y)} , which is concentrated around the maximum a posteriori estimate arg max x p ( x | y ) {\displaystyle \arg \max _{x}p(x|y)}
Diffusion_model
Method used in statistics, pattern recognition, and other fields
be estimated from the training set. Either the maximum likelihood estimate or the maximum a posteriori estimate may be used in place of the exact value
Linear_discriminant_analysis
Mathematical concept
A posteriori methods aim at producing all the Pareto optimal solutions or a representative subset of the Pareto optimal solutions. Most a posteriori methods
Multi-objective_optimization
Ability to automatically recognize targets
statistical estimation method such as maximum likelihood (ML), majority voting (MV) or maximum a posteriori (MAP) to make a decision about which target in the
Automatic_target_recognition
Number of values in the final calculation of a statistic that are free to vary
as intermediate steps in the estimation of the parameter itself. For example, if the variance is to be estimated from a random sample of N {\textstyle
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Technological framework
constructing a decision function include the maximum likelihood rule, the maximum a posteriori rule, and the minimum distance rule. In some cases, it may be better
Noisy_channel_model
Method of logical reasoning
about the distribution are updated with the observed sample, or maximum likelihood estimation (MLE), which identifies the distribution most likely given the
Inductive_reasoning
Distinction between nominal, ordinal, interval and ratio variables
limits were calculable a priori from a specification of the instrument. The second group could be calculated only a posteriori from a specification of what
Level_of_measurement
Statistical hypothesis test
significant difference (HSD) test, Newman Keuls test, Ducan's test "a posteriori comparisons"/ "post hoc comparisons"/ "exploratory comparisons"- choose
F-test
Recursive state estimator for state-space models derived via dynamic programming
p(y_{t}|{\hat {x}}_{t\mid t})} . The maximisation can be interpreted as a maximum a posteriori (MAP) estimate of x t {\displaystyle x_{t}} : it combines the log-likelihood
Bellman_filter
Object categorization problem
θ ∗ = θ M L {\displaystyle \theta ^{*}=\theta ^{ML}} ) or maximum a posteriori ( θ ∗ = θ M A P {\displaystyle \theta ^{*}=\theta ^{MAP}} ) procedure. However
One-shot learning (computer vision)
One-shot_learning_(computer_vision)
Statistical distribution for dependence between random variables
"Modelling of turbulent lifted jet flames using flamelets: a priori assessment and a posteriori validation". Combustion Theory and Modelling. 18 (2): 295–329
Copula_(statistics)
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
Male
French
French form of Latin Maximus, MAXIME means "the greatest."Â
Female
Spanish
Feminine form of Spanish PÃo, PÃA means "pious."
Female
Swedish
Short form of Swedish Linnéa, NÉA means "twinflower."
Female
Polish
Feminine form of Polish RadomiÅ‚, RADOMIÅA means "happy favor."
Female
Spanish
Feminine form of Spanish Estéban, ESTEFANÃA means "crown."
Female
Spanish
Spanish form of Roman Latin Lucia, LUCÃA means "light."Â
Female
Swedish
Swedish form of Latin Linnaea, LINNÉA means "twin flower."
Female
Polish
Feminine form of Polish LudmiÅ‚, LUDMIÅA means "people's favor."
Female
Slovene
Slovene form of Greek Hagne, NEŽA means "chaste; holy."
Boy/Male
American, Australian, Chinese, French, German, Greek, Latin, Swedish
Greatest
Female
Portuguese
Galician-Portuguese form of Hebrew Leah, LÃA means "weary."
Female
Thai/Siamese
Thai name A-GUN means "grape."
Female
Icelandic
Feminine form of Icelandic Stefán, STEFANÃA means "crown."
Female
Spanish
Spanish form of Greek Sophia, SOFÃA means "wisdom."
Male
Russian
(МакÑим) Variant spelling of Russian Maksim, MAXIM means "the greatest." Compare with another form of Maxim.
Female
French
French form of Hebrew Leah, LÉA means "weary."
Female
Egyptian
, a royal lady of the IIIrd or IVth dynasty.
Female
Polish
Feminine form of Polish BogumiÅ‚, BOGUMIÅA means "God-favor."
Female
Portuguese
Portuguese name GRAÇA means "graceful."
Male
Thai/Siamese
Thai name A-WUT means "weapon."
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
Boy/Male
Tamil
Reincarnated
Boy/Male
Indian
Shining, Lighting, Illuminating, Glitter, Flash, Luster, Bright
Girl/Female
Hindu
Another name of Goddess Parvati, Who is living in mountain
Boy/Male
Hindu, Indian
Full Moon; Complete; Renewed
Boy/Male
Scottish
Son of the carpenter.
Girl/Female
Hindu, Indian, Tamil
Goddess Earth; Wife of Sage Kashyap; Sweetest; Noisy; High Pitched; Swift Flowing; A Star
Boy/Male
Hindu
Praiser
Boy/Male
Tamil
Vaaridhar | வாரீதார
Cloud
Boy/Male
Hindu
Youth
Boy/Male
Australian, British, Chinese, Christian, English, German, Teutonic
Wolf
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
MAXIMUM A-POSTERIORI-ESTIMATION
n.
A coarse umbelliferous plant of Europe (Tordylium maximum).
n.
The least quantity assignable, admissible, or possible, in a given case; hence, a thing of small consequence; -- opposed to maximum.
a.
Later in time; hence, later in the order of proceeding or moving; coming after; -- opposed to prior.
n.
The greatest quantity or value attainable in a given case; or, the greatest value attained by a quantity which first increases and then begins to decrease; the highest point or degree; -- opposed to minimum.
n.
The state of being later or subsequent; as, posteriority of time, or of an event; -- opposed to priority.
a.
Greatest in quantity or highest in degree attainable or attained; as, a maximum consumption of fuel; maximum pressure; maximum heat.
pl.
of Minimum
pl.
of Maximum
n.
In a curve referred to polar coordinates, any point for which the radius vector is a maximum or minimum.
n. pl.
The hinder parts, as of an animal's body.
n.
Minimum.
n.
A posterior zygapophysis.
n.
A self-registering thermometer, especially one that registers the maximum and minimum during long periods.
a.
Situated behind; hinder; -- opposed to anterior.
a.
At or toward the caudal extremity; caudal; -- in human anatomy often used for dorsal.
a.
On the side next the axis of inflorescence; -- said of an axillary flower.
a.
Posterior.