The shifted log-logistic distribution is a probability distribution also known as the generalized log-logistic or the three-parameter log-logistic distribution. It has also been called the generalized logistic distribution, but this conflicts with other uses of the term: see generalized logistic distribution. (Wikipedia).
Graphing the logarithmic equation with a horizontal & vertical translation
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Graph a logarithmic equation and determine the domain and range
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Graphing logarithmic equations
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Finding the domain vertical asymptote and x intercepts of a logarithm
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Finding the domain asymptote and x intercept of a logarithm
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Learn to graph a logarithmic equation and find the x intercept
👉 Learn how to graph logarithmic functions involving vertical shift. The logarithmic function is the inverse of the exponential function. To graph a logarithmic function, it is usually very useful to make the table of values of the function. This is done by choosing a range of values of x
From playlist How to Graph Logarithmic Functions with Vertical Shift
Determining the inverse of exponential and logarithmic functions ex 2, y = 7^x
👉 Learn how to convert exponential equations to logarithmic equations. The logarithm of a number in a given base is the index/exponent to which the base must be raised to obtain the given number. In other words, log [base a] of x = m implies that a^m = x. Thus, when given an exponential eq
From playlist Condense and Expand Logarithms
Using the inverse of an exponential equation to find the logarithm
👉 Learn how to convert an exponential equation to a logarithmic equation. This is very important to learn because it not only helps us explain the definition of a logarithm but how it is related to the exponential function. Knowing how to convert between the different forms will help us i
From playlist Logarithmic and Exponential Form | Learn About
Statistical Rethinking Winter 2019 Lecture 14
Lecture 14 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. Covers Chapter 12: ordered categorical outcomes and ordered categorical predictors.
From playlist Statistical Rethinking Winter 2019
Transferring between exponential and logarithmic form
👉 Learn how to convert exponential equations to logarithmic equations. The logarithm of a number in a given base is the index/exponent to which the base must be raised to obtain the given number. In other words, log [base a] of x = m implies that a^m = x. Thus, when given an exponential eq
From playlist Condense and Expand Logarithms
Data Science - Part XV - MARS, Logistic Regression, & Survival Analysis
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview on extending the regression concepts brought forth in previous lectures. We wi
From playlist Data Science
Statistical Rethinking - Lecture 15
Lecture 15 - Ordered logit models (Monsters & Mixtures) - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
undergraduate machine learning 29: Neural nets and backpropagation
Neural networks. The slides are available here: http://www.cs.ubc.ca/~nando/340-2012/lectures.php This course was taught in 2012 at UBC by Nando de Freitas
From playlist undergraduate machine learning at UBC 2012
Lecture 14/16 : Deep neural nets with generative pre-training
Neural Networks for Machine Learning by Geoffrey Hinton [Coursera 2013] 14A Learning layers of features by stacking RBMs 14B Discriminative fine-tuning for DBNs 14C What happens during discriminative fine-tuning? 14D Modeling real-valued data with an RBM 14E RBMs are Infinite Sigmoid Beli
From playlist Neural Networks for Machine Learning by Professor Geoffrey Hinton [Complete]
Machine learning - Logistic regression
Logistic regression: Optimization and Bayesian inference via Monte Carlo. Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
From playlist Machine Learning 2013
Machine learning - Neural networks
Neural Networks Slides available at: http://www.cs.ubc.ca/~nando/540-2013/lectures.html Course taught in 2013 at UBC by Nando de Freitas
From playlist Machine Learning 2013
Statistical Rethinking - Lecture 13
Lecture 13 - Generalized Linear Models (intro) - Statistical Rethinking: A Bayesian Course with R Examples
From playlist Statistical Rethinking Winter 2015
Learning from Censored and Dependent Data - Constantinos Daskalakis
Computer Science/Discrete Mathematics Seminar I Topic: Learning from Censored and Dependent Data Speaker: Constantinos Daskalakis Affiliation: Massachusetts Institute of Technology; Member, School of Mathematics Date: March 9, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
How to determine the inverse of an exponential equation
👉 Learn how to convert an exponential equation to a logarithmic equation. This is very important to learn because it not only helps us explain the definition of a logarithm but how it is related to the exponential function. Knowing how to convert between the different forms will help us i
From playlist Logarithmic and Exponential Form | Learn About
CSE 519 -- Lecture 21, Fall 2020
From playlist CSE 519 -- Fall 2020