Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach

<div><svg height="12.5794pt" id="M171" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 32.5529 12.5794" width="32.5529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M828 650H570L564 622C646 614 650 609 634 524L604 366H253L281 524C296 608 308 615 382 622L388 650H132L126 622C211 615 214 609 198 524L121 122C106 42 102 35 23 28L17 0H276L282 28C196 35 191 40 206 122L243 324H597L559 123C544 42 537 35 452 28L446 0H710L716 28C632 35 628 43 643 122L719 524C735 610 741 615 822 622L828 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.075,3.132)"><path d="M329 433H203L239 587L230 596L147 534L123 433H57L30 395L34 388H115L61 129C37 16 59 -12 85 -12C147 -12 222 58 260 98L241 125C212 95 160 62 144 62C132 62 127 71 138 126L192 386L305 394L329 433Z"></path></g><g transform="matrix(.013,0,0,-0.013,13.743,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,18.241,0)"><path d="M377 665C166 665 44 494 44 317C44 126 183 -15 363 -15C547 -15 695 118 695 333C695 533 546 665 377 665ZM355 630C491 630 593 515 593 304C593 112 496 22 385 22C245 22 146 157 146 345S244 630 355 630ZM502 225V442H471C464 375 459 369 411 369H326C278 368 273 374 266 442H235V225H266C273 292 278 299 326 299H411C459 299 464 292 471 225H502Z"></path></g><g transform="matrix(.013,0,0,-0.013,27.848,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g></svg> curves of GAF. The red line is the <svg height="12.5794pt" id="M172" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 32.5529 12.5794" width="32.5529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M828 650H570L564 622C646 614 650 609 634 524L604 366H253L281 524C296 608 308 615 382 622L388 650H132L126 622C211 615 214 609 198 524L121 122C106 42 102 35 23 28L17 0H276L282 28C196 35 191 40 206 122L243 324H597L559 123C544 42 537 35 452 28L446 0H710L716 28C632 35 628 43 643 122L719 524C735 610 741 615 822 622L828 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.075,3.132)"><path d="M329 433H203L239 587L230 596L147 534L123 433H57L30 395L34 388H115L61 129C37 16 59 -12 85 -12C147 -12 222 58 260 98L241 125C212 95 160 62 144 62C132 62 127 71 138 126L192 386L305 394L329 433Z"></path></g><g transform="matrix(.013,0,0,-0.013,13.743,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,18.241,0)"><path d="M377 665C166 665 44 494 44 317C44 126 183 -15 363 -15C547 -15 695 118 695 333C695 533 546 665 377 665ZM355 630C491 630 593 515 593 304C593 112 496 22 385 22C245 22 146 157 146 345S244 630 355 630ZM502 225V442H471C464 375 459 369 411 369H326C278 368 273 374 266 442H235V225H266C273 292 278 299 326 299H411C459 299 464 292 471 225H502Z"></path></g><g transform="matrix(.013,0,0,-0.013,27.848,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g></svg> curve using the proposed E-E approach, and the black line is the <svg height="12.5794pt" id="M173" style="vertical-align:-3.29107pt" version="1.1" viewbox="-0.0498162 -9.28833 32.5529 12.5794" width="32.5529pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M828 650H570L564 622C646 614 650 609 634 524L604 366H253L281 524C296 608 308 615 382 622L388 650H132L126 622C211 615 214 609 198 524L121 122C106 42 102 35 23 28L17 0H276L282 28C196 35 191 40 206 122L243 324H597L559 123C544 42 537 35 452 28L446 0H710L716 28C632 35 628 43 643 122L719 524C735 610 741 615 822 622L828 650Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,10.075,3.132)"><path d="M329 433H203L239 587L230 596L147 534L123 433H57L30 395L34 388H115L61 129C37 16 59 -12 85 -12C147 -12 222 58 260 98L241 125C212 95 160 62 144 62C132 62 127 71 138 126L192 386L305 394L329 433Z"></path></g><g transform="matrix(.013,0,0,-0.013,13.743,0)"><path d="M300 -147C201 -63 143 98 143 270S200 602 300 686L282 710C136 610 70 450 70 271V270C70 89 136 -72 282 -170L300 -147Z"></path></g><g transform="matrix(.013,0,0,-0.013,18.241,0)"><path d="M377 665C166 665 44 494 44 317C44 126 183 -15 363 -15C547 -15 695 118 695 333C695 533 546 665 377 665ZM355 630C491 630 593 515 593 304C593 112 496 22 385 22C245 22 146 157 146 345S244 630 355 630ZM502 225V442H471C464 375 459 369 411 369H326C278 368 273 374 266 442H235V225H266C273 292 278 299 326 299H411C459 299 464 292 471 225H502Z"></path></g><g transform="matrix(.013,0,0,-0.013,27.848,0)"><path d="M275 270C275 450 212 609 64 710L45 686C145 604 203 442 203 270S147 -63 45 -147L64 -170C213 -68 275 89 275 270Z"></path></g></svg> curve using the classical Bayesian adaptive estimation.</div>

Computational Intelligence and Neuroscience

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Figure 5

Figure 5: Bayesian Adaptive Estimation with Theoretical Bound: An Exploration-Exploitation Approach 