{"slip": { "id": 207, "advice": "Always seek out advice or opinions when making a decision."}}
{"type":"standard","title":"Expectation–maximization algorithm","displaytitle":"Expectation–maximization algorithm","namespace":{"id":0,"text":""},"wikibase_item":"Q1275153","titles":{"canonical":"Expectation–maximization_algorithm","normalized":"Expectation–maximization algorithm","display":"Expectation–maximization algorithm"},"pageid":470752,"thumbnail":{"source":"https://upload.wikimedia.org/wikipedia/commons/thumb/6/69/EM_Clustering_of_Old_Faithful_data.gif/330px-EM_Clustering_of_Old_Faithful_data.gif","width":320,"height":275},"originalimage":{"source":"https://upload.wikimedia.org/wikipedia/commons/6/69/EM_Clustering_of_Old_Faithful_data.gif","width":360,"height":309},"lang":"en","dir":"ltr","revision":"1284886684","tid":"a56b0275-15f2-11f0-b514-00cace1deaf2","timestamp":"2025-04-10T10:00:35Z","description":"Iterative method for finding maximum likelihood estimates in statistical models","description_source":"local","content_urls":{"desktop":{"page":"https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm","revisions":"https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm?action=history","edit":"https://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm?action=edit","talk":"https://en.wikipedia.org/wiki/Talk:Expectation%E2%80%93maximization_algorithm"},"mobile":{"page":"https://en.m.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm","revisions":"https://en.m.wikipedia.org/wiki/Special:History/Expectation%E2%80%93maximization_algorithm","edit":"https://en.m.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm?action=edit","talk":"https://en.m.wikipedia.org/wiki/Talk:Expectation%E2%80%93maximization_algorithm"}},"extract":"In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem.","extract_html":"
In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step. It can be used, for example, to estimate a mixture of gaussians, or to solve the multiple linear regression problem.
"}{"type":"standard","title":"Wills Mountain State Park","displaytitle":"Wills Mountain State Park","namespace":{"id":0,"text":""},"wikibase_item":"Q8022477","titles":{"canonical":"Wills_Mountain_State_Park","normalized":"Wills Mountain State Park","display":"Wills Mountain State Park"},"pageid":19782949,"thumbnail":{"source":"https://upload.wikimedia.org/wikipedia/commons/thumb/f/f3/Alt_US_40_Cumberland_Narrows.jpg/330px-Alt_US_40_Cumberland_Narrows.jpg","width":320,"height":240},"originalimage":{"source":"https://upload.wikimedia.org/wikipedia/commons/f/f3/Alt_US_40_Cumberland_Narrows.jpg","width":1280,"height":960},"lang":"en","dir":"ltr","revision":"1250896330","tid":"322835e7-8927-11ef-a6ee-58494ba723ab","timestamp":"2024-10-13T05:51:31Z","description":"State park in Maryland, USA","description_source":"local","coordinates":{"lat":39.66861111,"lon":-78.78166667},"content_urls":{"desktop":{"page":"https://en.wikipedia.org/wiki/Wills_Mountain_State_Park","revisions":"https://en.wikipedia.org/wiki/Wills_Mountain_State_Park?action=history","edit":"https://en.wikipedia.org/wiki/Wills_Mountain_State_Park?action=edit","talk":"https://en.wikipedia.org/wiki/Talk:Wills_Mountain_State_Park"},"mobile":{"page":"https://en.m.wikipedia.org/wiki/Wills_Mountain_State_Park","revisions":"https://en.m.wikipedia.org/wiki/Special:History/Wills_Mountain_State_Park","edit":"https://en.m.wikipedia.org/wiki/Wills_Mountain_State_Park?action=edit","talk":"https://en.m.wikipedia.org/wiki/Talk:Wills_Mountain_State_Park"}},"extract":"Wills Mountain State Park is an undeveloped public recreation area on Wills Mountain overlooking the Cumberland Narrows in Allegany County, Maryland. The state park occupies 470 acres (190 ha) on the northwest edge of the City of Cumberland. It is under the control of the Maryland Department of Natural Resources.","extract_html":"
Wills Mountain State Park is an undeveloped public recreation area on Wills Mountain overlooking the Cumberland Narrows in Allegany County, Maryland. The state park occupies 470 acres (190 ha) on the northwest edge of the City of Cumberland. It is under the control of the Maryland Department of Natural Resources.
"}