bayesian

问题 I am struggling to understand how, in R, to generate predictive simulations for new data using a multilevel linear regression model with a single set of random intercepts. Following the example on pp. 146-147 of this text, I can execute this task for a simple linear model with no random effects. What I can't wrap my head around is how to extend the set-up to accommodate random intercepts for a factor added to that model. I'll use iris and some fake data to show where I'm getting stuck. I'll

问题 I need a library for naïve Bayes large scale, with millions of training examples and +100k binary features. It must be an online version (updatable after training). I also need top-k output, that is multiple classifications for a single instance. Accuracy is not very important. The purpose is an automatic text categorization application. Any suggestions for a good library is very appreciated. EDIT: The library should preferably be in Java. 回答1: If a learning algorithm other than naïve Bayes

问题 I am trying to implement Kalman filter. I only know the positions. The measurements are missing at some time steps. This is how I define my matrices: Process noise matrix Q = np.diag([0.001, 0.001] ) Measurement noise matrix R = np.diag([10, 10]) Covariance matrix P = np.diag([0.001, 0.001]) Observation matirx H = np.array([[1.0, 0.0], [0.0, 1.0]]) Transition matrix F = np.array([[1, 0], [0, 1]]) state x = np.array([pos[0], [pos[1]]) I dont know if it is right. For instance, if I see target

问题 I am trying to implement Kalman filter. I only know the positions. The measurements are missing at some time steps. This is how I define my matrices: Process noise matrix Q = np.diag([0.001, 0.001] ) Measurement noise matrix R = np.diag([10, 10]) Covariance matrix P = np.diag([0.001, 0.001]) Observation matirx H = np.array([[1.0, 0.0], [0.0, 1.0]]) Transition matrix F = np.array([[1, 0], [0, 1]]) state x = np.array([pos[0], [pos[1]]) I dont know if it is right. For instance, if I see target

问题 I have been looking for a multinomial naive Bayes classifier on CRAN, and so far all I can come up with is the binomial implementation in package e1071 . Does anyone know of a package that has a multinomial Bayes classifier? 回答1: bnlearn not doing it for you? http://www.bnlearn.com/ Is on CRAN, and claims to implement "naive Bayes" network classifiers and "Discrete (multinomial) data sets are supported". 来源： https://stackoverflow.com/questions/8874058/multinomial-naive-bayes-classifier

问题 I am currently working on a meta-analysis of survival data across several clinical trials. To do this, I have code from a published analysis using the same methodology. However, when running this code using the data from the published analysis, I am unable to replicate their results. In fact, the results fail to converge to any kind of reasonable estimate. The code itself (not including the data) should be correct as it comes directly from the authors. I assume the problem has to do w/

问题 Currently I try to minimize the function and get optimized parameters using GPyOpt. import GPy import GPyOpt from math import log def f(x): x0,x1,x2,x3,x4,x5 = x[:,0],x[:,1],x[:,2],x[:,3],x[:,4],x[:,5], f0 = 0.2 * log(x0) f1 = 0.3 * log(x1) f2 = 0.4 * log(x2) f3 = 0.2 * log(x3) f4 = 0.5 * log(x4) f5 = 0.2 * log(x5) return -(f0 + f1 + f2 + f3 + f4 + f5) bounds = [ {'name': 'x0', 'type': 'discrete', 'domain': (1,1000000)}, {'name': 'x1', 'type': 'discrete', 'domain': (1,1000000)}, {'name': 'x2'

问题 I'm trying to apply the Bayesian rating formula, but if I rate 1 out of 5 thousand of hundreds, the final rating is greater than 5. For example, a given item has no votes and after voting 170,000 times with 1 star, its final rating is 5.23. If I rate 100, it has a normal value. Here is what I have in PHP. <?php // these values came from DB $total_votes = 2936; // total of votes for all items $total_rating = 582.955; // sum of all ratings $total_items = 202; // now the specific item, it has no

问题 Is there a Bayesian filter library for .NET? I would like to setup a group of folders and have emails automatically moved to those folders based on what has been previously moved to the folder. If you are familiar with FogBugz auto-sort, that's exactly what I would like to do. 回答1: I found Weka.Net which is a port of the Weka Java Machine Learning Framework to .NET. It is an alpha release, and seems not so active, but if it has the core of Weka, you can use it to write what you ask for.

问题 Is there a Bayesian filter library for .NET? I would like to setup a group of folders and have emails automatically moved to those folders based on what has been previously moved to the folder. If you are familiar with FogBugz auto-sort, that's exactly what I would like to do. 回答1: I found Weka.Net which is a port of the Weka Java Machine Learning Framework to .NET. It is an alpha release, and seems not so active, but if it has the core of Weka, you can use it to write what you ask for.