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Próxima revisión 		Revisión previa
clase:iabd:pia:documentacion [2022/02/22 13:04]
admin [Compendios] 		clase:iabd:pia:documentacion [2024/05/09 15:15] (actual)
admin [Estadística Bayesana]
Línea 4: Línea 4:
  
  
-===== Regresión logística ===== 
  
-    * [[https://medium.com/analytics-vidhya/logistic-regression-part-i-transformation-of-linear-to-logistic-395cb539038b|Logistic Regression Part I — Transformation of Linear to Logistic]] +
-    * [[https://hausetutorials.netlify.app/posts/2019-04-13-logistic-regression/|Gentle intro to logistic regression]] +
-    * [[https://medium.com/nerd-for-tech/understanding-logistic-regression-782baa868a54|Understanding Logistic Regression]] +
-    * [[https://arunaddagatla.medium.com/maximum-likelihood-estimation-in-logistic-regression-f86ff1627b67|Maximum Likelihood Estimation in Logistic Regression]] +
-    * [[https://medium.com/analytics-vidhya/logistic-regression-a3249301b75e|Logistic Regression]]: Explica cosas de L1 y L2 +
-    * [[https://towardsdatascience.com/the-intuitive-explanation-of-logistic-regression-a0375b1bee54|The Intuitive Explanation of Logistic Regression]] +
-    * [[https://medium.com/gadictos/logistic-regression-a-detailed-overview-from-scratch-ad0491f14c3b|Logistic Regression: A Detailed Overview from Scratch]]+
  
  
Línea 88: Línea 81:
   * [[https://towardsdatascience.com/transformers-explained-visually-not-just-how-but-why-they-work-so-well-d840bd61a9d3|Transformers Explained Visually — Not just how, but Why they work so well]]   * [[https://towardsdatascience.com/transformers-explained-visually-not-just-how-but-why-they-work-so-well-d840bd61a9d3|Transformers Explained Visually — Not just how, but Why they work so well]]
   * [[https://samarthagrawal86.medium.com/feature-engineering-of-datetime-variables-for-data-science-machine-learning-45e611c632ad|Feature Engineering of DateTime Variables for Data Science, Machine Learning, Python]]   * [[https://samarthagrawal86.medium.com/feature-engineering-of-datetime-variables-for-data-science-machine-learning-45e611c632ad|Feature Engineering of DateTime Variables for Data Science, Machine Learning, Python]]
 +
 +
 +===== Lenguaje Natural =====
 +  * [[https://paperswithcode.com/method/bert|BERT]]
 +  * [[https://www.codificandobits.com/blog/bert-en-el-natural-language-processing/|BERT: el inicio de una nueva era en el Natural Language Processing]]
 +
 +Word Embedding Castellano:
 +  * [[https://github.com/aitoralmeida/spanish_word2vec]]
 +  * [[https://www.kaggle.com/rtatman/pretrained-word-vectors-for-spanish]]
 +  * [[https://github.com/dccuchile/spanish-word-embeddings]]
 +  * [[https://crscardellino.ar/SBWCE/]]
  
  
Línea 112: Línea 116:
   * [[https://rohit10patel20.medium.com/hyperparameter-search-using-bayesian-optimization-and-an-evolutionary-algorithm-bdca6331de1c|Hyperparameter search using Bayesian Optimization and an Evolutionary Algorithm]]   * [[https://rohit10patel20.medium.com/hyperparameter-search-using-bayesian-optimization-and-an-evolutionary-algorithm-bdca6331de1c|Hyperparameter search using Bayesian Optimization and an Evolutionary Algorithm]]
   * [[https://towardsdatascience.com/how-to-use-horovods-large-batch-simulation-to-optimize-hyperparameter-tuning-for-highly-a815c4ab1d34|How to Use Horovod’s Large Batch Simulation to Optimize Hyperparameter Tuning for (Highly) Distributed Training]]   * [[https://towardsdatascience.com/how-to-use-horovods-large-batch-simulation-to-optimize-hyperparameter-tuning-for-highly-a815c4ab1d34|How to Use Horovod’s Large Batch Simulation to Optimize Hyperparameter Tuning for (Highly) Distributed Training]]
-  * Teorema de Bayes 
-    * [[https://seeing-theory.brown.edu/bayesian-inference/es.html|Viendo la Teoría - Inferencia Bayesiana]] 
-    * [[https://datascience.com.co/c%C3%B3mo-funciona-la-inferencia-bayesiana-dc4ad29d4697|¿Cómo funciona la inferencia bayesiana?]] 
-    * [[https://distill.pub/2020/bayesian-optimization/|Exploring Bayesian Optimization]]: Muy buena explicación de la optimización Bayesiana 
-    * [[https://picanumeros.wordpress.com/2021/04/18/la-estadistica-detras-del-rescate-de-la-bomba-de-palomares/|La estadística detrás del rescate de la bomba de Palomares]] 
-    * [[https://towardsdatascience.com/bayes-theorem-the-holy-grail-of-data-science-55d93315defb|Bayes’ Theorem: The Holy Grail of Data Science]] 
-    * [[https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962|Probability Learning II: How Bayes’ Theorem is applied in Machine Learning]] 
-    * {{ :clase:iabd:pia:a_gentle_introduction_to_bayesian_analysis.applications_to_development_research.pdf|A Gentle Introduction to Bayesian Analysis.Applications to Development Research}}  
  
 +
 +===== Estadística Bayesana =====
 +
 +  * [[https://seeing-theory.brown.edu/bayesian-inference/es.html|Viendo la Teoría - Inferencia Bayesiana]]
 +  * [[https://datascience.com.co/c%C3%B3mo-funciona-la-inferencia-bayesiana-dc4ad29d4697|¿Cómo funciona la inferencia bayesiana?]]
 +  * [[https://distill.pub/2020/bayesian-optimization/|Exploring Bayesian Optimization]]: Muy buena explicación de la optimización Bayesiana
 +  * [[https://picanumeros.wordpress.com/2021/04/18/la-estadistica-detras-del-rescate-de-la-bomba-de-palomares/|La estadística detrás del rescate de la bomba de Palomares]]
 +  * [[https://towardsdatascience.com/bayes-theorem-the-holy-grail-of-data-science-55d93315defb|Bayes’ Theorem: The Holy Grail of Data Science]]
 +  * [[https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962|Probability Learning II: How Bayes’ Theorem is applied in Machine Learning]]
 +  * {{ :clase:iabd:pia:a_gentle_introduction_to_bayesian_analysis.applications_to_development_research.pdf|A Gentle Introduction to Bayesian Analysis.Applications to Development Research}} 
 +  * [[https://en.wikipedia.org/wiki/Pre-_and_post-test_probability|Pre- and post-test probability]]
 +
 +
 +
 +
 +  * {{ :clase:iabd:pia:doing_bayesian_data_analysis.pdf |Doing Bayesian Data Analysis:A Tutorial with R, JAGS,and Stan}}
 +    * [[http://doingbayesiandataanalysis.blogspot.com/2013/08/how-much-of-bayesian-posterior.html|How much of a Bayesian posterior distribution falls inside a region of practical equivalence (ROPE)]]
 +    * {{ :clase:iabd:pia:bayesian_estimation_supersedes_the_t_test.pdf|Bayesian Estimation Supersedes the t Test (PDF)}}
 +      * [[https://jkkweb.sitehost.iu.edu/BEST/|Bayesian estimation supersedes the t test]]
 +      * [[https://www.sumsar.net/best_online/|Bayesian Estimation Supersedes the t-test (BEST)]]: Online tool
 +      * [[https://xianblog.wordpress.com/2016/12/05/bayesian-parameter-estimation-versus-model-comparison/|Bayesian parameter estimation versus model comparison]]: Critica de "Bayesian estimation supersedes the t test"
  
  
Línea 356: Línea 373:
  
 </sxh> </sxh>
 +
 +===== Calculo de errores =====
 +  * {{ :clase:iabd:pia:2eval:propagacion_de_errores.pdf |Propagación de errores}}
 +  * {{ :clase:iabd:pia:2eval:guia_practica_para_la_realizacion_de_la_medida_y_el_calculo_de_errores.pdf |Guía práctica para la realización de la medida y el cálculo de errores}}
 +
 +
 +===== Intervalos de confianza =====
 +
 +  * Teoria
 +    * [[https://sebastianraschka.com/blog/2022/confidence-intervals-for-ml.html|Creating Confidence Intervals for Machine Learning Classifiers]]
 +    * [[https://hmong.es/wiki/Wilson_score_interval|Intervalo de confianza de la proporción binomial]]
 +    * {{ :clase:iabd:pia:2eval:intervalos_de_confianza_para_las_estimaciones_de_proporciones_y_las_diferencias_entre_ellas.pdf |Intervalos de confianza para las estimaciones de proporciones y las diferencias entre ellas}}
 +    * {{ :clase:iabd:pia:2eval:capitulo_5.inferencia_estadistica_ii_estimacion_por_intervalo_de_confianza.pdf |Capitulo 5.Inferencia Estadística II: Estimación por Intervalo de Confianza}}
 +    * {{ :clase:iabd:pia:2eval:intervalos_de_confianza_e_intervalos_de_credibilidad_para_una_proporcion.pdf |Intervalos de confianza e intervalos de credibilidad para una proporción}}
 +    * {{ :clase:iabd:pia:2eval:bioestadistica_para_no_estadisticos.08_intervalos_de_confianza.pdf |Bioestadistica para no estadisticos. Intervalos de confianza}}
 +    * {{ :clase:iabd:pia:2eval:understanding_and_interpreting_confidence_and_credible_intervals_around_effect_estimates.pdf |Understanding and interpreting confidence and credible intervals around effect estimates}}
 +  * Herramientas:
 +    * [[https://www.causascientia.org/math_stat/ProportionCI.html|Exact Confidence Interval for a Proportion]]:Herramienta online para calcular intervalos de confianza con el teorema de bayes
 +    * [[https://epitools.ausvet.com.au/ciproportion|Límites de confianza para una proporción]]: Herramienta online para calcular intervalos de confianza.
 +  * Código Python
 +    * [[https://www.statsmodels.org/v0.12.2/generated/statsmodels.stats.proportion.proportion_confint.html|statsmodels.stats.proportion.proportion_confint]]: Calculo en Pyhton de intervalos de confianza.
 +    * [[https://akshay-a.medium.com/confidence-interval-for-population-proportion-basic-understanding-in-python-56b8cc5f8320|Confidence Interval for Population Proportion basic understanding in python]]
 +
 +===== Estadística bayesiana y precision =====
 +  * {{{{ :clase:iabd:pia:2eval:las_pruebas_pcr_con_elevada_sensibilidad_y_especificidad_en_condiciones_de_alta_prevalencia_o_bajo_prescripcion_medica_son_fiables.pdf |Las pruebas PCR con elevada sensibilidad y especificidad, en condiciones de alta prevalencia o bajo prescripción médica, son fiables}}
 +  * {{ :clase:iabd:pia:2eval:6-ayuda_pruebas_diagnsticas.pdf |Pruebas diagnosticas}}: Explicación desde el punto de vista médico de Bayes con la especificidad y sensibilidad
 +  * {{ :clase:iabd:pia:2eval:falsos_negativos_en_tests_de_covid-19.pdf |Falsos negativos en tests de COVID-19}}
 +  * {{ :clase:iabd:pia:2eval:commentary_sensitivity_specificity_and_predictive_values_foundations_pliabilities_and_pitfalls_in_research_and_practice.pdf |Commentary: Sensitivity, Specificity,and Predictive Values: Foundations,Pliabilities, and Pitfalls in Research and Practice}}
 +  * {{ :clase:iabd:pia:2eval:comentario_sensibilidad_especificidad_y_valores_predictivos_fundamentos_flexibilidades_y_dificultades_en_la_investigacion_y_la_practica.pdf |Comentario: Sensibilidad, especificidad y valores predictivos: fundamentos,flexibilidades y dificultades en la investigación y la práctica}}: El documento anterior pero traducido al castellano.
 +  * Prevalencia
 +    * {{ :clase:iabd:pia:2eval:riesgo_de_contagio_por_covid-19_en_funcion_del_tipo_de_contacto_y_de_la_renta_familiar.pdf |Riesgo de contagio por COVID-19 en función del tipo de contacto y de la renta familiar}}
 +    * {{ :clase:iabd:pia:2eval:prevalencia_de_infeccion_por_coronavirus_sars-cov-2_en_pacientes_y_profesionales_de_un_hospital_de_media_y_larga_estancia_en_espana.pdf |Prevalencia de infección por coronavirus SARS-CoV-2 en pacientes y profesionales de un hospital de media y larga estancia en España}}
 +
 +===== Probabilidad del resultado de la red =====
 +  * {{ :clase:iabd:pia:2eval:introduction_to_uncertainty_in_deep_learning.pdf |Introduction to Uncertainty in Deep Learning}}
 +  * {{ :clase:iabd:pia:2eval:calibrar_modelos_de_machine_learning.pdf |Calibrar modelos de machine learning}}
 +  * [[https://wttech.blog/blog/2021/a-guide-to-model-calibration/|A guide to model calibration]]
 +
 +  * {{ :clase:iabd:pia:2eval:predicting_good_probabilities_with_supervised_learning.pdf |Predicting Good Probabilities With Supervised Learning}}
 +  * {{ :clase:iabd:pia:2eval:neural_network_prediction_scores_are_not_probabilities.pdf |Neural Network Prediction Scores are not Probabilities}}
 +  * [[https://machinelearningmastery.com/calibrated-classification-model-in-scikit-learn/|How and When to Use a Calibrated Classification Model with scikit-learn]]
 +  * scikit-learn
 +    * [[https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html|Probability Calibration curves]]
 +    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html|sklearn.calibration.calibration_curve]]
 +    * [[https://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html|sklearn.calibration.CalibratedClassifierCV]]
 +  * Regresión logística isotónica
 +  * Calibración de Platt
 +
 +===== PyMC3 =====
 +  * {{ :clase:iabd:pia:2eval:bayesian_linear_regression_in_python_using_machine_learning_to_predict_student_grades_part_2.pdf |Bayesian Linear Regression in Python: Using Machine Learning to Predict Student Grades Part 2}}
 +
  
  
clase/iabd/pia/documentacion.1645531471.txt.gz · Última modificación: 2022/02/22 13:04 por admin

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